I recently attended the Genpact AI conference, where the Genpact employees I spoke to were energized by the changes AI is bringing and are focused on helping clients operationalize emerging technologies at scale. The company is investing in tools to provide greater ongoing feedback from employees: an HR executive described how they use an employee feedback system combined with a benefit awards system (like an airline's rewards program) to monitor employee satisfaction and identify ways to remedy shortcomings.      

In this blog, I look at Genpact’s approach to scaling AI across the enterprise.

Scaling AI across the enterprise

Genpact’s AI focus is on the “AI of Now”. It believes that for AI to have an impact, it needs to be scaled operationally across the enterprise. Unlike many competitors, Genpact did not demonstrate futuristic AI functionality but instead focused on how it helps drive adoption across enterprises, presenting examples of operational deployment of AI to six clients. To grow its client base, Genpact wants to do more work with Tier Two enterprises, which typically have a more significant portion of their operations delivered with legacy platforms and manual processes.

To drive operational adoption of AI across an enterprise, Genpact believes there are three things required:

• Domain expertise: understanding industry-specific processes and data from working with those processes. Genpact has identified which processes and data it will work with for AI projects   
• Technology ecosystem and expertise: building an ecosystem of product vendors is necessary to access emerging AI functionality. Executing successful implementations requires the tools, integrators, and technology knowledge that most clients do not have    
• Dynamic talent: building an effective workforce to deliver these services requires training and creating cross-functional data, AI, and domain experts’ teams.

Practical AI applications

To build its AI services, Genpact surveyed what CFOs want from their technology investments, and found their top requirements to be:

• Actionable insights
• Reliable forecasts
• Talent that can implement and run AI effectively.

Based on this research, Genpact has embedded AI into its F&A offerings to enable CFOs to improve capital allocation and produce more reliable sales and profit forecasts with on-demand revenue and cost forecasts, fast decision-making with what-if analyses, and the ability to drive change in the trajectory of their business. Using these tools, enterprise clients can:

• Drive growth through AI-driven insights and data-backed decision-making
• Improve revenue forecasting, working capital optimization and planning
• Improve operational efficiency with AI-driven automation. 

Genpact can develop better AI-based insights than any single client because it draws on a large pool of data from clients across multiple industries. Genpact’s business supporting F&A draws on its experience doing 500 quarterly book closes for 35k legal entities annually. The data and domain experience from this sizeable annual transaction pool enable robust predictive analysis and the ability to apply AI using keystroke-level process knowledge, thus enabling it to deliver outcomes to its clients. Similarly, Genpact has applied its considerable operational transaction experience to address supply chain and bank fraud challenges for clients.  

New AI tools

At the conference, Genpact announced three proprietary tools for its AI ecosystem:

• Genpact CFO Actions Hub: the four key themes the hub will address are responsible AI, fine-tuning LLMs, the agent-computer interface, and how to retrieve & generate data that enables CFOs to transform data into cohesive narratives and enable forward-looking actions like scenario planning and forecasting. It uses LLMs and Genpact’s domain knowledge on a foundation of responsible AI to drive relevance with CFOs
• Genpact Agentic AP Solutions:  the launch of four AI-based AP solutions, already live, with four AI-based AR and four AI-based accounts solutions coming soon  
• Genpact Finance AI Academy: a set of training courses for employees and clients to improve their domain/technology expertise for the use of AI in finance.

In addition, Genpact’s GenAI solution, Scout, was on the conference app. It summarized each presentation soon after it had been delivered. This was a significant aid to this conference attendee because of the speed at which the summaries were sent out after each session. Presentation slides were also available on the app soon after each session.

Conclusions

Genpact’s AI strategy is to drive operational adoption of AI within enterprises to deliver business value. Operational adoption requires both client and Genpact employees to become familiar with AI technology and how it works in practice. Success in AI deployment means work practices will change, eventually making historical operations architectures irrelevant. Successful change management will need employee buy-in, and Genpact is building continuous feedback mechanisms to keep employees on board.  

In July, I published a blog outlining the latest evolving practices in core banking platform transformation. In this new blog, I outline Capgemini's approach to addressing core banking transformation challenges with its clients.

The imperative of platform transformation

Demand for core banking platform transformation continues to proliferate to enable banks to:

• Reduce their operational costs
• Meet customer expectations and increase C-SAT
• Migrate operations to the cloud
• Enable FinTech functionality, including AI and GenAI
• Increase operational agility and bring new offerings to market faster
• Integrate operations into the emerging digital ecosystem and comply with open banking requirements 
• Achieve faster and more accurate adoption of changing compliance requirements
• Improve risk management capabilities
• Achieve effective international expansion. 

However, bankers are reluctant to start transformation projects due to many key challenges, including:

• Difficulty in running an operation environment while undertaking a renovation project
• High risk of project failure leading to career failure
• Complexity, cost, and long timelines to run a project
• Limited access to skilled resources to execute a project.

Capgemini’s approach to core banking transformation

Capgemini has developed a disciplined core banking transformation methodology to address the challenges to achieving a successful transformation based on six critical success factors. It addresses each of these success factors in its engagements as follows:

Effective governance model:

A project governance model must enable project lifecycle continuity. The governance team must include all relevant stakeholders, including the IT group, LOB owners, and third-party service providers. Bringing in the right decision-makers for each microservice is critical to keeping a project on track. Retaining an active governance board will mitigate the risk of project failure over time as people change roles.

Identifying functionalities embedded in the core platform:

Most legacy systems have poor functionality documentation. Banks need to identify functionalities, dependencies, and integrations. BIAN is the standard reference for an industry-standard banking framework. This process needs to be executed with speed and accuracy. Capgemini employs its IPs: CAP360, a legacy code analyzer, and BREAD, a GenAI legacy rules extractor. These tools typically deliver a 40% cost reduction in identifying functionalities versus manual identification.

Sequencing the decomposition of functionalities:

Their dependence on ledger systems must drive the sequencing of pulling out functions into modules. Banks should begin by decoupling systems, such as customer management, where multiple functionalities converge to deliver service. The second set of functions to be decoupled are the ones not dependent on ledger systems (e.g., compliance and payments). Finally, the bank should decouple functions dependent on the ledger systems.        

Prioritizing orchestration investments:

A microservices environment needs to be able to link the modules to execute business processes. Rolling out an orchestrator requires prioritizing which functionalities to orchestrate first and what customization to build into the orchestrator.  

Reinventing the target operating model:

To reinvent and execute a new operating module, the bank must assign the right team to own and manage individual modules continuously. It needs to coordinate how teams work to enable collaboration across modules and align their development roadmaps.

Linking business value to the transformation journey:

Identifying, implementing, and reporting agreed-upon KPIs and SLAs enables the stakeholders to remain committed to the project and identify remedies if objectives are not met.  

Case study

Capgemini recently undertook a core banking transformation project for a tier-one global financial institution offering retail, wealth, corporate, and SME banking services in 50 worldwide markets.

Challenges:

• Long time-to-market for new bank products 
• Higher customer servicing costs and inability to meet customer expectations due to a lack of digital banking capabilities
• Increasing inefficiency and high maintenance costs of heterogeneous and end-of-life legacy system operations
• The need to componentize a microservice-based architecture to be able to bring in best-of-breed modules 
• Inflexibility of existing system.

Scope of services provided:

To support the bank’s discovery phase and baseline the operations architecture, Capgemini delivered architecture components including:

• Capability model covering all the capabilities aligned to the BIAN model
• Requirements catalog covering 2000 requirements aligned to the capability model
• Baselined scope, defining the markets, products, processes, and integrations to be covered
• Architectural blueprint covering designs for all infrastructure components.  

Capgemini set up a Design authority covering:

• Architecture principles, critical design decisions, RAID Log, and non-functional requirements
• Data migration and coexistence strategy, migration options, co-existence scenarios, reconciliation and cut-over approach
• Minimum Viable Product (MVP ) definition approach and sample MVPs
• Roadmap for the next phase of architecture and design
• Functional, nonfunctional, and architectural inputs to the vendor selection RFI for selecting a new core product vendor.

Benefits:

• Defined core and non-core capabilities with ownership agreed with 50 teams across the globe for delivering non-core capabilities
• Employed a BIAN-aligned capability framework and a roadmap to define BIAN-aligned microservices
• Developed a global technology architecture that identified the applications that will deliver non-core functionalities
• Enabled componentized modules, which can  be extended to all the bank brands and accommodate local requirements
• Identified existing initiatives the bank will need to change to align with the core banking transformation initiative
• Developed an operational  drivers value map to identify the go-to-market proposition for the first MVP
• Identified the core banking product vendors with whom the bank would like to send an RFP
• Delivered a platform and technology perspective for the target state reference architecture
• Delivered a detailed design for the core banking transformation program.

Conclusions

In summary, most banks are pursuing a hollow-the-core strategy for core banking transformation. However, the success of that approach is dependent on bringing all relevant stakeholders to oversee the project and the ongoing evolution of operations. Rigorous prioritization of module sequencing based on business goals and process dependencies will drive value enablement; and orchestrating technology modules and operations units will deliver value to customers.       

Banks are undertaking core banking transformation projects at an accelerating pace. Typically, two-thirds of these transformation projects will fail. Most either fail to implement a new solution altogether, leaving the bank with the original legacy system, or (in the case of a successful implementation), fail to deliver the anticipated value. Some projects, such as those at TSB Bank and Cooperative Bank, cause permanent impairment to the business and become the stuff of legend.

Since the risk of failure is high, why are almost all banks undertaking core platform transformation projects today?  Driving the change is a rapidly evolving industry facing multiple challenges:

• Regulatory changes that require real-time transaction processing, open banking businesses, and expanded reporting, especially for fraud and risk 
• Changing customer bases resulting from generational change and the banking of previously unbanked consumers
• New bank entrants, both established banks entering new markets and digital bank startups
• Technological change, including cloud, FinTech, AI, and distributed ledger
• High-cost structures requiring greater efficiency to enable those banks to remain viable.

To address these challenges, banks are pursuing three goals with their transformation efforts: developing low-cost access to quality resources, eliminating internal barriers to all corporate resources, and increasing operational efficiency.  

Low-cost access to resources

Banks are pursuing the goal of providing low-cost access to resources by building ecosystems of vendors and partners with the help of technology service providers. To succeed, their platforms need to be able to plug in products and services from the ecosystem partners, so the banks are enabling optimum internal technology deployments by modularizing functionality within their platforms.

To do this, banks need a tech/business talent ecosystem to staff transformation projects. Finally, they need an ecosystem of third-party business partners, such as independent investment advisors, loan originators, and data vendors, to set up new businesses quickly and meet open banking requirements.    

Eliminating internal barriers to corporate resources

Eliminating internal barriers to corporate resources is being achieved by rearchitecting core banking platforms into a microservices architecture, which enables the bank to change the operational structure from product silo-based to customer-centric. A customer-centric architecture allows the bank to deliver hyper-personalization of services to each customer. Critical to successfully removing internal barriers is changing how data is managed by improving data sourcing, scrubbing, and efficiency. Finally, embedding AI into a platform that can access all relevant data enables customers to shop complex offering portfolios more easily.

Increasing operational efficiency

The third goal of improving efficiency is being achieved by digitalizing all processes and documents. Banks are outsourcing more processes to convert CapEx to OpEx to align revenues and costs better. Processes are being automated with either digitalization or RPA. Where manual execution is still required, banks are implementing AI to reduce manual error, increase the span of control, and deploy consistent use of best practices.     

Summary

The biggest challenge to achieving these goals is implementation risk, and the biggest challenge to successful implementation is change management. Banks mostly avoid full replacement strategies in favor of phased modernization or functionality decoupling. These strategies are easier to pursue using cloud delivery, which reduces change management risks by moving technology change management to the cloud provider, leaving business change management as an internal task.

Third-party vendors can provide best practices from other engagements in the industry, which is especially helpful to regional and local banks. These best practices are evolving rapidly as FinTech and cloud technology continue to evolve.

I will publish a market assessment on transforming core banking services in September to delve deeper into this market. It will identify how the market is evolving, what services banks are buying to support their transformation efforts, and the benefits being realized.

Financial institutions are data-driven businesses, and because of decades of investment in technology, banks process data using heterogeneous legacy environments. Modern AI and GenAI solutions promise to enable banks to manage and analyze data more effectively. However, adopting new AI solutions is lagging far behind the market hype. In this blog, I look at how TCS is helping clients address this challenge.

The Challenge

Banks must modernize their data management practices and technology infrastructure to adapt to fast-changing regulations, business models, stakeholders, and technology offerings. The scale and complexity of bank legacy data environments are a primary inhibitor to data modernization.

Most existing AI and GenAI projects are point solutions that deliver some benefits but cannot deliver business transformation. Further, when an AI solution is implemented, the highly siloed nature of large banks makes data analysis ineffective because modern AI, ML, and GenAI require the analysis of very large data sets.

For banks to adopt AI at the pace and scale needed to drive fundamental business transformation requires support from technology services providers. Essential third-party tools needed include:

• Taxonomies, frameworks, and use cases to identify where and how to implement AI solutions
• Ecosystems of vetted FinTech vendors to draw on for emerging functionalities
• Accelerators to drive effective implementation.  

TCS’ Approach to AI Enablement

TCS has multiple offerings specific to BFSI customers and their requirements around AI. For example, it has developed Advanced Quantz & Analytics, an offering to enable clients to accelerate their AI journey, delivering services comprising:

• Technology and analytics engineering
• Business contextualization
• AI strategy consulting
• Design
• Innovation.

These five services are delivered as a package to identify combined business/technology requirements and implement transformative change. However, to reduce complexity and time to operational deployment, TCS is developing use cases and templates for AI deployment.

To support the development of use cases, templates, and offering development, the Advanced Quantz & Analytics team has built four COEs:

• Applied data science: delivers AI/ML solutions at the enterprise level 
• Language and semantics: delivers GenAI and Deep Learning knowledge graph applications by partnering with graph producers 
• Quantz: delivers quantitative solutions for market and risk use cases   
• Analytics engineering: delivers process and ecosystem transparency for ML-based operations and orchestration. 

These COEs have developed use cases that are segmented by time to deployment and business value:

• Time to deployment:
  • 0 to 6 months: quick implementation to achieve rapid payback
  • 6 to 12 months: medium-term implementation, which can be scaled across LOBs, silos, and markets 
  • Over 12 months: transformational engagements requiring major platform retooling
• Business value:
  • ROI
  • Customer impact
  • User adoption rate. 

TCS has developed ten categories of ML/AI models it wants to work with in BFSI, including sales, risk, financial forecast, and language models that are already fully operational, and others that are in development. 

Advanced Quantz & Analytics has developed 147 use cases across eight asset classes, with credit and equity having the highest number of use cases developed to date. To operationalize use cases, clients have access to TCS’ ecosystem of 1k technology providers and 500 FinTechs in TCS’ COIN ecosystem.     

TCS is working with 80 clients in BFSI to deliver AI services with the Advanced Quantz & Analytics offering. Most clients are large banks able to draw on large data sets, but many engagements require TCS to deploy synthetic data sets to enable effective ML and analysis.

Summary

AI and GenAI offer new power to enhance the value of bank data and transform many financial services business models. Identifying relevant use cases and implementing effective solutions remains challenging for the banking industry. TCS has developed an offering to support banks deploying AI and GenAI effectively and quickly. Its Advanced Quantz & Analytics offering has a roadmap for developing new use cases and toolsets to enable the offering to mature as AI technology continues to develop quickly.  

I recently attended the EY Global Analyst Summit 2024, the theme of which was Rethink! The conference sought to answer the question, “How is EY rethinking the value it delivers?” and this blog looks at how EY is rethinking its activities in support of the BFS industry sector.

EY has been growing its business rapidly, with the most recent numbers showing 15% revenue growth for the FY year ending June 31, 2023. EY is rethinking and doubling down on its transformation offerings to grow its business further and is investing in two areas (IP and offerings) to enable differentiated delivery of transformation projects.  

Intellectual Property  

EY is focusing its IP investments on accelerators, frameworks, and platforms, and is developing business solutions in partnership with ISVs and FinTechs. EY’s key platform offerings are:

• EY Fabric: a global foundational technology platform providing reusable assets, standards, and access to IP for all EY technology projects. 69k EY clients are using Fabric with 2m unique users globally
• EY Nexus: a technology platform that combines EY’s industry knowledge and implementation experience to help their clients push the boundaries of new ideas, accelerate problem solving and simplify business operations ​
• EY Canvas: a unified audit platform that digitalizes the entire audit process
• EY.ai: a platform supporting enterprises looking to implement AI, providing access to solutions, people, and methodologies. Ey.ai has 100 AI apps deployed, 10k dedicated EY employees, and receives 330k visits per month.

The key benefits banking clients derive from these platforms are the ability to:

• Standardize platform transformation for global enterprises across multiple LOBs and markets. For global banks, this allows units to maintain independence and local market compliance while still enabling centralized risk management and CX to adhere to the bank’s global standards   
• Reduce cost and time to market on implementation. One banking case study provided claimed a 70% reduction in implementation effort for a global bank due to the combined use of AI and RPA embedded in the implementation platform.          

Offerings

EY has eleven categories of service offerings. The conference highlighted two offerings in the BFS industry: managed services and sustainability.

Managed services are its most recently created offering category (see my blog “EY Managed Services Driving Increased Efficiency for Clients” from June, 2022). Managed services have been growing faster than EY’s expectations and currently over 500 managed services clients are buying four or more managed services solutions from EY. Since June 2022, EY has added three new lines of managed services: customer & growth, supply chain, and technology services. 

Managed Services

Managed Services recently acquired a FDL, which provided one of the clients with centralized data management taxonomies, practices, and services across its disparate businesses. EY will build the FDL capability into a high-end managed data services offering for global enterprises, enabling them to:

• Create a unified data model across their businesses
• Build an integrated data-ingestion-to-user experience
• Integrate AI into their data processes
• Build persona-based data products.  

Global banks, which have diverse business segments and customer bases with highly diverse businesses, are expected to become a large client base for the managed data service offering.  

Sustainability

EY’s sustainability practice has delivered 17k engagements for 11k clients with a dedicated Climate Change and Sustainability Services team of 4K professionals. EY has applied and learned from its internal sustainability activities and reduced the EY carbon emissions by 43% from 2019 to 2023 while growing its headcount by 40%. The practice provides ESG and Sustainability technology services across:

• Reporting and performance management
• Green IT and responsible computing
• Sustainable supply chain and operations
• Decarbonization and net zero transformation. 

Sustainability for BFS services focuses on reporting and Green IT, and it will soon add three services to support banks' lending activities and sourcing decisions. Sustainability services for BFS are delivered from the managed services unit. After several years of breakneck growth, BFS represents the largest single industry segment in EY’s sustainable managed services.   

BFS Industry Offerings

EY’s BFS industry cloud offering is Nexus, a platform for financial institutions looking to digitalize their operations. Launched in 2020, Nexus runs on clients’ choice of cloud provider infrastructure and comprises industry-focused products packaged to address market and client challenges. ​One way in which banks are using Nexus is to accelerate their adoption of modern core platforms. To develop emerging functionality for banks, EY is working with alliance and ecosystem partners to develop joint offerings, starting with a lower-cost lead generation solution.   

An example of Nexus’ recently delivered large-scale banking cloud migration engagements is where a consortium of four New York-headquartered global banks set up a cloud-based processing system for syndicated loans and now processes 65% of all syndicated loans. Based on Microsoft, the platform uses blockchain technology to enable transparency down to the loan level. The platform has reduced settlement times from weeks to days.

EY has three focus areas for growth in BFS: technology modernization, risk management, and sustainable development; and it has built the tools for rapid delivery to improve processing in these three areas. EY recently acquired a FDL, enabling it to deliver the data management capabilities to populate these three focus areas with better data and analytics. The FDL capability will allow it to accelerate the growth of its current offerings for BFS. 

EY is working on several initiatives to provide banks with better access to and use of their existing data:

• Data analysis to reduce time to value for mergers. The banking industry expects to accelerate the pace of mergers over the next few years. To support their clients, EY

developed a comprehensive suite of FinTech solutions to help scale acquisition strategies and service models as well as foster organic growth and engagement within financial institutions’ customer base leveraging the partnership with MoneyLion and its embedded marketplace infrastructure technology, data insights and content solutions

• Enabling wealth advisors to use complex internal data to improve customer offers. In one case, a large wealth manager wants to integrate Open AI into its platforms to provide its advisors with enhanced access to internal data 
• FIS, the core banking platform and solutions vendor, wants to migrate its applications to the cloud. Cloud delivery will enable FIS to expand the range of clients and markets it sells to. EY, in partnership with Microsoft, has developed migration accelerators. Six solutions went live on the cloud in December 2023.   

Summary

In summary, EY’s banking practice is doubling down on offerings that migrate platforms to the cloud, making more data accessible to a broader range of users and delivering managed services to clients. These new offerings will allow EY to reach out to markets and clients, such as mid-size firms, where it has not been active. Managed services and industry-specific transformations are the fastest-growing segments of its BFS business and promise to be less cyclical than its traditional professional services business.       

Financial institutions have complex portfolios of products presented to customers in a siloed, product-centric fashion, which makes shopping for financial products inefficient and reduces the overall customer experience. However, digital delivery promises to improve customers' shopping experience, CX, and CSAT. 

Some firms in other industries have been doing this successfully, such as Disney for its theme parks. Each theme park has a complex set of offerings to choose from, and Disney uses its MagicBand to connect consumers to all their choices within the park and deliver the final experience. However, developing a customer-centric, unified shopping experience for financial services remains an industry challenge. In the early 2000s, Citibank tried to address these issues with its “Financial Supermarket” concept, But by 2008, the Citibank experiment famously failed. 

Capgemini’s client-centricity framework

Capgemini and Salesforce are working together to enable enterprises to deliver customer-centric, unified, and optimized consumer experiences for financial services, similar to the Disney MagicBand concept. Capgemini has built a client-centricity framework to identify customer drivers and enterprise enablers that will support reengineered customer engagement, and in turn, drive revenue growth and improve customer satisfaction. Critical components of Capgemini’s client-centricity framework include:

• Customer drivers:
  • Personalization: tailoring the customer’s experience 
  • Availability: ubiquitous, 24/7 access and responsiveness to customer inquiries/demands  
  • Trust: built via reputation, ethics, and actions
  • Diverse offerings: providing a wide range of financial offerings  
  • Autonomy: empowering customers to make informed decisions  
• Enterprise enablers:
  • Strategy Alignment: ensuring corporate strategy aligns with customer needs  
  • Data capabilities: employing data to customize offerings to individual customers
  • AI/technology upgrade: re-engineering the technology platform to a customer-centric approach   
  • Operating model automation: digitalizing and automating all operations delivery to customers  
  • Culture: attracting, training, and retaining employees with high domain knowledge and customer interaction skills. 

Effective organizations can analyze customer drivers in real-time and prioritize offerings and fulfillment to meet a customer’s needs.  

Salesforce technology

Salesforce provides the solutions and platforms to integrate and deliver enablement for clients. Key Salesforce offerings used in the partnership include:

• Data Cloud: the platform integrates sales, service, and marketing data from multiple silos to create a single experience across customer-facing functions
• Sales Cloud: a sales management solution providing lead and pipeline management, forecasting, and AI insights
• Service Cloud: a customer services platform that helps businesses manage and resolve customer inquiries and issues
• Marketing Cloud: the platform integrates data, teams, technology, and AI to enable real-time personalization
• Experience Cloud: enables a business to create branded digital experiences to share information and collaborate   
• Financial Services Cloud: a platform built on the SF Sales and Service Cloud platforms but customized for the financial services industry, including industry compliance requirements and data compliance. Initially designed for the wealth management industry, it supports relationship managers managing customer relationships 
• Einstein 1 and Co-pilot Studio: AI assistants that respond to natural language inquiries
• Next Gen Tableau: automates data analysis, predicts user requirements, and generates actionable insights.

These solutions provide functionality to enhance data quality and integrity, provide contextual suggestions, and customize AI actions to specific customers.

By combining these two offerings, Capgemini’s client-centricity offer and Salesforce’s Sales and Experience Cloud offerings, financial institutions can deliver hyper-personalized customer experiences based on their individual preferences. The AI embedded in these platforms enables advisors and customers to find available financial products that are relevant. Current engagements are delivering these capabilities to banks. Here is a recent example:

Client example: large U.S. bank

Challenge

A leading retail bank could not deliver the seamless customer experience it wanted. Specific challenges included:

• Fragmented customer experience due to the lack of a unified digital solution. Lack of a 360° customer overview resulting in a poor time-to-market for new product launches
• Fragmented operational silos, which created a disjointed customer journey, the slow release of solution enhancements, and reconfiguration challenges
• Manual interventions to communicate data across silos reduced visibility and increased error rates in market and transaction data. 

Scope of Services

• Integrated and deployed an integrated solution of Salesforce, Mulesoft, and Marketing Cloud across all systems
• Solution was delivered across experience strategy, roadmap development, design, implementation, and standup
• Phased deployment strategy to minimize risk
• New activation channels were opened based on predefined segmentation to enhance customer engagement.  

Benefits

• Customers experienced an omnichannel experience with priorities based on customer goals, regardless of initial channel entrance
• Improved lead management, resulting in increased conversion ratios
• Minimal impact on legacy systems
• Elimination of manual processes leading to lower error rates, faster throughput, and enhanced CSAT
• Integration benefits included real-time reporting to speed up troubleshooting, provide insights into customer behavior, and assess system performance.

As this case demonstrates, customers no longer have to “stumble upon” products that meet their needs by accessing multiple product silos; the platform can sift through an ocean of data to narrow down offerings and present a few highly relevant suggestions for final consideration. This should make dealing with a bank less stressful and more satisfying.          

NelsonHall recently attended the Infosys U.S. Analyst Day in Dallas, the theme of which was “Being AI First”. It demonstrated clear progress since last year’s conference in Infosys’ thinking and approach to the critical steps for effective implementation and operational deployment of AI.

Key components of AI-led operational transformation

Infosys and its clients attending the conference identified three essential components to enabling an effective AI-led operations transformation:

• Stakeholder interaction and co-development of problem definition, goals identification, roadmap development, and operations transformation
• Data hygiene, including sourcing, scrubbing, QA, and distribution
• Segmentation, identifying which data, processes, and projects will remain managed internally and which are moved externally.  

To drive the first component of stakeholder interaction, Infosys pursues a hub-first strategy for innovation. Hubs are operational centers of expertise where Infosys and its clients work on client projects to develop novel POCs and operational deployments. Clients prefer to be involved in innovating their operations and have relevant input to shape and drive the innovation process. This enables a co-creation process that allows enterprises to focus on innovation at a higher success rate. Infosys values the feedback, domain expertise, and deep knowledge of an institution’s differentiating factors that clients provide in a hub environment. 

To drive innovation in a tightly coupled co-creation environment, Infosys has built tech and innovation hubs worldwide. In the U.S., it had committed to building four hubs in 2018, but it has set up six because of client demand. The six hubs are based in Providence, RI; Richardson, TX; Hartford, CT; Raliegh, NC; Indianapolis, IN; and Phoenix, AZ.

The ideation from hubs needs to be realized with AI functionality and delivered with operational flexibility. To enable that synthesis, Infosys has coupled two of its platforms, Topaz (an AI-first offering that helps enterprises create value from generative AI technologies) and Cobalt (a set of services, solutions and platforms for enterprises to accelerate their cloud journey), to speed up ideation and the operationalization of successful POCs. This enables innovation at both speed and scale.

To continue growing its capabilities, Infosys is investing in AI to support operational transformation.  Infosys’ significant investments in AI-led transformation are:

• Infrastructure-led transformation to the cloud
• Data and AI transformation leveraging the cloud
• Business transformation led by enterprise apps and SaaS
• Platform engineering
• Non-IT workloads to the cloud.

Infosys’ AI-first strategy for financial services

Infosys has built its most comprehensive domain-specific AI capabilities for the financial services industry. Financial services is the largest industry segment for Infosys (~30% of revenues), and North America is the largest single market (~60% of total revenues).

Infosys’ AI-first strategy for financial services focuses on nine areas it uses to help banks improve their business performance. The focus areas are:

• Personalization at scale for sales and marketing
• Deepening relationships by helping advisors work with customers
• Portfolio management and product design and testing
• Risk scoring for credit, AML, KYC, and onboarding
• Operations improving CX and employee experience
• Modernizing tech and infrastructure with AI
• Talent and change management
• Making the enterprise data ready.

Client use case

Infosys provided a use case of a credit union. The credit union has been committed to giving back to its employees and members, and had to identify how changing technology and member preferences could be fulfilled and achieved with new delivery techniques and offers. Data is the core of the financial services industry, And the credit union segmented its data into three areas:

• Foundational data: this is unchangeable data that should remain on-premises. Examples include identifier data and legally private data   
• Core data: this data includes transaction data and transient entity data. Examples include KYC, AML, fraud, and transaction data. This data can be moved to the cloud 
• Sales and marketing data: this data changes rapidly in response to changing market conditions and various product campaigns. This data should be kept in the cloud from the start because it has a very short lifespan. However, the research data supporting the campaigns comes from internal and external data and, therefore, should not be kept in the cloud.  

Infosys identified 200 use cases to manage the data and drive better customer value. Cases were ideated, POCed, and operationalized in a disciplined waterfall where unpromising use cases were dropped. The primary driver of use cases that make it to full operational deployment is customer data rather than product data.  The client started the initiative with employees and leveraged Infosys’ tools and support to enable it to develop offerings and delivery methodologies that appeal to a younger generation with differing priorities and needs than the older generation.       

Summary

Intelligent automation and data transformation POCs and projects have gained traction over the past year, propelled by the GenAI opportunity and hype. Only some of the many POCs and MVPs have translated into operational transformation at scale. Infosys’ approach to using AI to drive operational change uses client co-creation to build differentiating operational change, data hygiene techniques to enable effective analysis, and rigorous segmentation of data, projects, and processes to set role-based responsibilities. This approach allows for change to happen quickly and at scale.      

Infosys provided examples of how it delivers these services in the financial sector, including regional and local banks. These banks represent a larger addressable market for IT services vendors than tier-one banks because they have older legacy systems and will be less likely to retain as high a level of internal operations as the tier-one banks in the long run.

The challenge for vendors is to identify compelling value propositions for clients. Infosys has addressed this challenge with its localization initiatives that drive the co-creation of differentiated operational offerings at scale. Data drives the financial services industry, and Infosys’ activities with a credit union, described above, outline how a data transformation program can be handled.  

ESG services are an emerging set of tracking and reporting capabilities for enterprises. Emerging technologies do not mature in a straight line but cycle through peaks and valleys of development and adoption as they mature. In the past year, ESG services have passed from the euphoria stage of market adoption to the valley stage as the hype has been confronted by the real-world challenges of failure to meet expectations and outright fraud in some cases.  

I am currently conducting a market assessment of how ESG services are transforming the banking sector, and here are some early findings.

ESG in Banking: early findings

The financial services industry is an early adopter of ESG reporting due to regulations that set implementation deadlines, and because of the data-heavy nature of the business.

Adoption is currently immature but is growing rapidly. Financial institutions are applying 60% of their efforts to environmental services, 30% to social services, and 10% to governance services. Social and governance initiatives are growing faster than environmental initiatives.

Currently, 70% of banks’ ESG initiatives are internal business activities, 20% investing and lending, and 10% supply chain activities (banks have small supply chains relative to manufacturing, wholesale, and retail).

Also, 80% of ESG activities have been focused on reporting, primarily for regulatory purposes. The remaining 20% of activities have focused on mitigating adverse ESG outcomes. As financial institutions are enabling better reporting, they are now accelerating their mitigation efforts.     

Adoption is highest in Europe because its regulations are stricter than in other geographies, with earlier implementation deadlines. North America has the second highest level of ESG adoption. Regulations for ESG adoption in North America and Europe generally follow the same principles, with Europe implementing its regulatory deadlines earlier and with higher remediation hurdles. The rest of the world lags behind these two markets in adoption and has far less consistency in applying principles.  

The adoption of ESG services in banking is concentrated in the following processes:

• Green IT: banks do not engage in manufacturing but manage large amounts of data. As such, their primary activities are delivered by IT systems. Reducing a bank’s carbon footprint in IT services is the primary path to increasing sustainability in bank operations
• Inclusion in lending/saving: a large percentage of all populations do not have formal banking relationships. Banks are reaching out to underbanked people as part of their social initiatives to provide banking services to unbanked consumers. The country with the highest level of activity in this area in India
• Rating investments based on ESG criteria for wealth and asset managers. This has been driven by regulation and customer preference. Sourcing, managing, and reporting the data is a massive undertaking, the buildout of which remains ongoing
• Rating carbon emissions data for real estate (buildings). Banks own, invest in, and finance real estate. Sourcing, managing, and reporting this data is more mature than for investments, but it is still an area that is growing
• Governance: financial institutions have fiduciary responsibilities to customers and regulators. Adapting their internal governance systems to deliver better ESG performance has been an area of growing importance over the past several years. Governance engagements are focused on consulting and change management activities.    

Areas of emerging adoption include:

• Green lending: banks are experimenting with lending programs that provide a reduced interest rate when the borrower meets specific ESG requirements
• Standardizing metrics for evaluating the sustainability of suppliers and investments. Much of the pushback on ESG comes from using metrics that do not accurately reflect a company’s sustainability, such as greenwashing. Banks and ESG services vendors are working to identify relevant data and scrub it to make it a better indicator of an enterprise’s actual sustainability    
• Standardizing the approach to ESG across lines of business and countries. Currently, multi-national banks have very different approaches to ESG issues across these silos. Banks are moving to standardize their approach to enable brand integrity and build a globally optimum response to ESG challenges. 

Many ESG engagements are enhancing banks’ data management capabilities to assess and report on emissions. Below are two examples.

Case 1: British multinational bank and Capgemini    

• Challenge: The client wanted to modernize its ESG data operating model and rationalize its data feeds on the emissions in its lending portfolio     
• Scope of services:
  • Built an ESG data store to measure the bank’s financed emissions
  • Enabled tracking at the portfolio level
  • Designed target operating model to deliver greater flexibility to changing regulations
  • Designed data governance framework   
• Benefits:
  • Reduction in data sources: 50%
  • Cost savings: $1m per year in third-party data sourcing spend
  • Improved tracking and analytics capabilities

Case 2: Large capital markets firm and Infosys  

• Challenge: The firm wanted to enhance its ESG data management for investments, and wanted to be able to:  
  • Ingest ESG investment data, validate it, and prepare it to be consumable at the issuer level
  • Manage the large and variable number of structured and unstructured ESG data sources            
• Scope of services:
  • Created roadmap, conducted downstream impact analysis on consumption of ESG
  • Vendor solution validation 
  • Implementation of ESG investments platform on AWS cloud
  • Implemented NLP-based AI algorithms to support both structured and unstructured data
• Benefits:
  • Productivity gain in data analysis: 50%
  • Mapped 5k issuers
  • Validated statistical accuracy of 400 ESG metrics

In December, I will be publishing a market assessment of ESG activities in banking, “Transforming the Banking Industry with ESG Services” which will delve deeper into how this global initiative is developing, what banks are doing to address these challenges, and how technology services vendors are supporting this transformation. Though this is a transformation that is still in its early stages, banking is one of the industries making the change quicker due to the regulatory deadlines it faces and its high use of IT services in all its business lines.  

NelsonHall has just completed the research for a market assessment and forecast report on Transforming Intelligent Automation Services in Banking. We found that banks have accelerated the adoption of IA services in operations over the past two years, a shift that has been driven by:

• Declining operating margins on legacy businesses
• Pursuit of new customer demographics to replace the older customer cohort, which requires banks to launch and deliver new products digitally
• Changing business models (primarily to open banking) driven by regulations.

By transitioning operational delivery from manual to digital processing, financial institutions can drive:

• Greater brand integrity from consistent execution
• Faster time to market for new and seasonal products
• Reduced error rates which reduce costs and enable settlements on shortened deadlines
• Improved data management, which enables hyper-personalization.

IA adoption pattern

Tier-one banks have been the primary adopters, with every tier-one bank having an active IA program.  Regional banks and industry services providers are increasing their commitment to IA projects. Only 30% of regional banks had IA programs three years ago; today, 75% of these banks have active IA programs. Local banks are where regional banks were three years ago, with 30% having active IA programs. Capital markets firms are late adopters but are beginning to adopt IA services to serve high-net-worth customers and meet reduced settlement deadlines. 

The pace of lower-tier banks starting IA programs is accelerating, and three years from now we expect all financial institutions to be fully committed to IA in their business. The growth in clients and project scope will drive growth rates of IA revenues to 16.5% per year for the next five years to 2028. Managed services for IA operations will grow 17% over the forecast period. Growth has been fastest in mature markets, but now the emerging and APAC markets are growing faster than mature ones.   

Financial institutions are focusing their efforts on the following:

• Operationalizing IA use cases: banks are deemphasizing the building of POCs to find ways to use IA and are instead concentrating on identifying high-impact use cases that can be operationalized to create value
• Improving data management: banks are changing their focus from improving data management within silos to improving data sourcing, management, and reporting across silos
• Expanding the pool of skilled employees to implement IA: banks are focusing on implementing tools (such as low/no code) that enable non-technical employees with domain expertise to build IA solutions with business impact at lower transaction volumes 
• Enabling human/bot coordination and increased effectiveness: these initiatives are the least mature. Currently, banks are coordinating human/bot routines on customer-facing activities (i.e., customer contact, advisory, and lending origination). Banks are beginning to explore human/bot coordination in back-office settings, including settlements, reconciliations, and collateral management.  

How vendors are enabling IA initiatives

Vendors are helping clients accelerate their IA initiatives by delivering enablement in four areas:

• Process identification and mapping: undertaking comprehensive process discovery, reengineering, automation, and embedding intelligence into processes that were previously unmapped and manual. The goal is to expand the footprint of automation in operations until all processes are digital or at least touched by digital services 
• Data management: delivering three critical areas of data management:
  • Data sourcing and scrubbing, focusing on standardizing data across a bank’s silos of products, markets, and sub-industries  
  • Expanding the range of data banks utilize into voice, image, and unstructured data
  • Implementing predictive AI to support customer interactions and service  
• Productizing IA offerings: the IA market has developed quickly. Most vendors have capabilities but not productized offerings. Vendors have developed productized offerings in the past two years to support lower-tier financial institutions and bank subsidiaries. These target clients are looking for rapid deployment of best practice capabilities at low cost
• Workforce effectiveness: delivering three areas of improvement:
  • Developing IP, which supports employees and stakeholders in delivering IA projects with less training and fewer technical skills. This type of IP includes COEs, accelerators, APIs, low/no-code solutions, and solution libraries
  • Training, including online, in-person, hackathons, and academies
  • Coordination of worker/bot teams with user journey design tools and training.

Outlook

IA services in BFS are continuing to evolve new offerings and use cases. Developments include:

• New regulations, including:
  • Open banking regulations, which will necessitate automating processes with third parties. This will involve integrating processes across counterparties not part of the initial financial institution. Integrating and automating processes across enterprises raises the challenge of coordinating governance
  • Instant payments, which require increased automation to reduce error rates and faster settlement
  • Faster securities settlement times, which require reduced error rates and faster settlement
  • Application of relevant regulations to customers as they move across markets with differing regulations
• Data management, including:
  • Management of third-party vendors and agents
  • Use of increasingly powerful analytics requiring it to be used judiciously as well as effectively
• Increased application of IA to the omnichannel environment to:
  • Increase customer choice of how to interact with banks
  • Support customer interpretation of offers with visual and audio information
  • Support the financial institution in understanding customer requirements and sentiment from real-time feedback analysis
• Expanding automation activities from customer-facing processes to back office and fulfillment processes. Automation to date has focused on customer interactions. Automation and AI will increasingly be applied to fulfillment processes and integrated into front-office processes.

Increasingly, business value from IA projects is less about cost savings and more about business agility. The ability to bring products to market faster and provide operational support for rapidly changing offerings is more important than cost savings on sunsetting offerings.

Capgemini has partnered with Microsoft to develop and market a dynamic hyper-personalization offering for the financial services industry. This blog explores how hyper-personalization capabilities can be adapted to the unique needs of the financial services industry.

Background

Enterprises want to increase the personalization of their offerings to customers to increase CSAT and sales success. However, effective personalization is an immature art. Enterprises have been using machine learning (ML) to drive the personalization of sales and marketing offerings to their customers. Still, the challenge of ML here is that the user can only generate incomplete information due to the iterative nature of the trials and limited contextual data. The ML bot can only observe the response to a chosen action but doesn't know the answer for other possible actions (i.e., it cannot analyze hypotheses, it can only analyze historical activities).    

One method of increasing the effectiveness of personalization initiatives is using contextual-based ML techniques to improve recommendations. ‘Contextual bandits’ is a technique that reinforces the learning algorithm by using contextual information about the environment to make real-time decisions and using rewards at each step. Microsoft has embedded this technique in its Azure Personalizer solution, which enables enterprises to improve their personalization efforts to achieve better CSAT and sales closure.

 The Challenge

Financial services is a lagging adopter of hyper-personalization capabilities. Early adopters are consumer industries, including CPG, consumer electronics, and media. Financial services lag in adoption due to the following:

• Heterogeneous customer bases: at the retail level, banks mix consumers and SMBs into a single practical customer base, which often has the same decision-maker, but differing needs and behaviors
• Multiple sales centers: M&A has led to multiple silos selling and competing with each other with the same products for the same customers. Banks continue to be organized by product lines, which also compete for the same clients from product silos.

These characteristics of the banking environment have made traditional AI and ML techniques a poor predictor of consumer behavior. If banks can apply additional parameters and data to understand consumer behavior better, they can develop improved personalized offers.  The challenge in achieving this level of analysis is that the banks need to:

• Integrate data across channels, silos, and sources
• Access data across heterogeneous systems and environments
• Better understanding of customer needs
• Deliver personalized, integrated offers across channels.  

Capgemini’s Approach 

Capgemini and Microsoft have developed an offering to address these challenges.  The contributions from the two companies are:

• Capgemini - IT and business services:
  • Financial services solutions and accelerators aligned with Microsoft offerings
  • IT services for Azure deployments and Microsoft Cloud for Financial Services
  • Access to industry specialist ecosystem
• Microsoft - three software products:
  • Azure Personalizer, which provides modeling tools and algorithms to contextualize a user’s content reactions
  • Semantic Knowledge Graph, which extracts relationships from data and derives features for use in AI models
  • Azure Synapse, a cloud-based analytics tool.

Financial institutions have had difficulty effectively implementing the infrastructure to drive hyper-personalization initiatives. Capgemini works with Microsoft and clients to drive this forward across multiple silos and LOBs. Critical to Capgemini’s activities is the ability to build systems that:

• Use real-time data
• Render the data analysis useable (using the Knowledge Graph)
• Personalize the data
• Develop a real-time trial offer
• Roll out successful offers
• Learn from mistakes in real-time.

Critically, the offering should be able to offer customers the best existing product and suggest new products for development. Suggesting product development is especially important for industry transitions, including the deployment of new regulations and changes in customer buying trends (for example, due to demographic differences). The offering also works with SMB customers, not just consumers.

This offering fills a need for financial institutions to help create personalized offerings for customers, especially from new demographic groups. Financial institutions use AI to analyze past data within silos to develop customized offerings but have not yet been able to create forward-looking offerings in response to changing conditions. This offering promises to enable banks to dynamically test and evolve their offerings quickly to meet a changing market.    

In the financial services industry, there was an initial expectation that demand for intelligent automation (IA) would fall off as clients changed platforms and moved to a microservices architecture. Instead, the application of IA in financial services has grown rapidly. The initial evidence from my current study shows that despite challenges deploying the technology effectively, the ability to bring decision making to the line-of-business user at a low cost of implementation makes a compelling value proposition.

Despite its ability to accelerate accurate, transparent process execution, implementing IA effectively is challenging. Key challenges include:

• Use case failure rate: the majority of use cases have not met their promised returns. Most POCs have not been operationalized. Increasingly, ITS vendors have had to build COEs and use case libraries to increase the cost effectiveness of IA projects. Success rates are rising, but only where ITS vendors are able to marry client operational knowledge with industry best practice  
• Orchestration of bots: operations is a dynamic environment and bots are unable to reskill or change jobs without advanced orchestration services. Performance degrades rapidly in a poorly orchestrated environment  
• Dynamic nature of business processes, including cyclicality, reengineering, and sunsetting: the tempo and structure of operations changes with time, requiring continuing process reengineering and new use case development
• Heterogeneous core banking platforms: most banks today are an amalgam of multiple platforms. Deploying automation across multiple environments and architectures requires process discovery, reengineering, and consolidation.  

Despite these challenges, IA activities have taken off because:

• Banks lack sufficient investment capital for full platform replacement
• Bots have benefited from increasing functionality, mostly due to increased AI capabilities, which enable them to deliver greater value. For example, banks need to increase their employee productivity. The AI used for bots has been used to develop human/tech coordination capabilities which have enabled better customer interactions and improved internal software development 
• Technology services vendors and banks have improved their use case validation methods, leading to greater success in operationalizing initiatives.  

Over the past several years, as automation services have matured, banks have begun narrowing the number of processes they are spending resources on automating. Processes where IA is increasingly applied are:

• Technology development, especially software development 
• Customer contact, especially human/bot delivered contact; often focusing on support for independent agents (such as third-party mortgage originators or independent wealth advisors)
• Cyclical processes, such as loan originations/collections, payments, and portfolio due diligence
• Compliance, with a heavy focus on KYC and AML processes  
• Data management, especially in mining new sources of data (from social media and third-party data vendors).

IA adoption will continue to grow faster than overall technology adoption in the financial services industry because banks need to move fast to deal with rapidly declining margins and the need to change their business models. Automation vendors are remaining relevant in this developing market by investing heavily in building more AI functionality. Vendors who are struggling are the ones who have failed to embed robust new AI functionality in their offerings. Ultimately, intelligent automation is less about process automation and more about helping banks orchestrate their labor forces and business partners to deliver service to customers.    

NelsonHall recently completed a market assessment and forecast report on Transforming Financial Services with Cloud, SaaS, and BPaaS Services. It reveals that the financial services industry is responding to the industrialization of cloud services from hyperscalers and IT services vendors by transforming its platforms to microservices architectures and then moving them to the cloud. Data management across markets, businesses, and entities has moved to center stage to drive compliance and customer management.

The operational transformation challenge

Tier 1 banks are looking for increasing operational agility with migration to cloud and as-a-service delivery. Smaller banks require productized solutions and SaaS/BPaaS services. All banks are increasing their data management and AI purchases. In the future, banks will move towards SaaS and BPaaS to reduce labor costs and increase the configurability of their businesses. Operational delivery will become agile to support reducing time to market and accommodate volume fluctuations.

However, the external environment has put up barriers to transformation. The key barriers impeding banks’ transformation efforts include:

• Platform modernization: legacy mainframe platforms need to be redesigned into a microservices architecture. Finding teams that can work with both legacy and modern languages is difficult
• Data management and analysis: effective use of AI requires large databases from which to derive insights. Most bank portfolios are too small to generate large data sets for meaningful analysis
• Accessing emerging technologies: skilled labor remains in short supply and worker turnover has made it worse
• Creating a roadmap: new business models require experimentation and agility, which large banks find difficult to undertake effectively. 

Financial institutions must adapt transformation strategies

To transform their businesses, financial institutions will have to:

• Create a strategy that moves operations to the cloud and matches costs to revenues to enable greater agility. They will have to change their business model to an open banking model
• Build an ecosystem of operational vendors including: hyperscalers, with each one providing services in their area of expertise; IT services vendors with knowledge of the client’s environment; and FinTechs with key functionality
• Redefine the split of operations between external processes (high value, cyclical or one-time) and internal processes (lower value/less volatile)
• Acquire orchestration tools to manage a heterogeneous environment of vendors and products
• Transform application development to a DevOps and low/no code model to speed the innovation cycle and enable LOB staff to have greater input.

Services vendors are supporting clients with different services for each environment:

• Cloud: support for entering new markets and enabling open banking  
• SaaS: data management tools, analytic tools, and data and solution provider partnerships
• BPaaS: offerings focused by line of business which have high volatility (e.g., collections and securities)
• Platforms: provision of low code/no code DeFi coding capabilities, APIs to solution/data vendors and industry consortia, enhanced solutions with hyperscaler partnerships, and orchestration tools to manage hybrid multi-cloud environments.

Summary

In summary, high competition and regulatory change is driving banks to focus on changing their business models and product mix. The change to shorter product cycles and lower margins means banks changing their operational focus from cost efficiency for fundamentally static businesses to agility for continuously changing businesses. Cloud, SaaS, and BPaaS infrastructure will drive accelerating change in banks’ product offerings, customer base, and market presence. The financial industry is at the start of a long-term transformation of its business model. 

Find out more about NelsonHall’s Transforming Financial Services with Cloud, SaaS, and BPaaS Services market assessment and forecast report here or contact Guy Saunders.

2022 has been a very strong year for IT services firms delivering cloud, digital, and AI services to the financial services industry. 2023 looks to be a very different year for these vendors as they are finding it difficult to hire skilled resources, find clients able to fund new large projects, and partner in strategic growth areas. Successful BFS technology consultants will need to focus limited resources in a few key growth areas to have a successful business in 2023. One strategic area for growth will be implementing transaction processing AI and data management tools.

To date, banks have focused their AI efforts on entity or account analytics. Typically, these tools provide  customer service (e.g., customer onboarding and next-best recommendations) and compliance support (e.g., AML/KYC). We believe in 2023 banks will turn their compliance and customer analytics focus from entity analytics to transaction analytics. Financial institutions are driven by evolving regulations, which today are emerging faster from Europe than other geographies. Europe has several new regulations which are driving a change in focus to transaction monitoring. These include:

• The Digital Operations Resiliency Act (DORA): DORA seeks to mitigate the risk of cyber attacks on financial operations. Previous regulations took a balance sheet-based approach to risk mitigation by requiring capital buffers to address operational risk. DORA takes an operational approach to mitigating risk by requiring technology (i.e., cloud, supply chains, and IT outsourcers) to operate with transparency, rigor, and resiliency. The act was published on 11/17/2022, will come into force by Q1 2023, and financial institutions will be required to have implemented it by Q4 2024.    
• A pilot regimen for market infrastructure in DLT: Passed June 2, 2022 by the EU, this regulation covers trading of currencies/securities using blockchain technology in multilateral trading facilities and settlement systems
• Regulations governing real-time payments/transactions: regulations include PSD2 and various securities settlement regulations, which have been shortening the settlement windows for various securities transactions towards an ultimate goal of instant transactions. Platforms need to move from batch processing to real-time processing with improved security and risk management to mitigate fraud and credit risk. An example of real-time payments implementation is the launch scheduled for summer 2023 of FedNow, the Federal Reserve bank’s instant payment service.          

These regulations will drive securities exchanges, payments networks, banks, and capital markets firms to deploy cloud orchestration and AI FinTech tools to improve security and reduce operational risk. Already financial institutions are starting projects to address these challenges. Examples include:

• Goldman Sachs Transaction Bank (GS TxB): On Sept. 15, 2022 GS TxB announced a partnership with Stripe to provide corporate treasury services with embedded finance (the ability to make payments as part of the treasury application). This service requires APIs and AI to manage and deliver transaction flows safely and securely.
• State Street Bank launching a peer-to-peer repo program for the buy side to reduce the cost of collateral management, which is a critical trading cost. This facilitates bilateral trading by counterparties with varying credit and capital strength.

Implementing these transaction-oriented FinTech solutions is more complex than account-based solutions because transaction-oriented solutions require orchestration and transparency across the entire network infrastructure. These projects will be driven by the market exchanges and tier one institutions, but will require cooperation from all market participants.

The result will be a more robust financial infrastructure with smaller participants benefiting from the move to an open banking environment for transaction processing. The promise of safe digital payments in a decentralized environment will not be achievable without these investments in securing the industry infrastructure. The industry and regulators are committed to delivering on the promise, so the implementation work will start in 2023.      

Industry background

Financial services are very centralized, with exchanges, central banks, and custodians delivering platform-based services to any user who wants to make a financial transaction or own a financial asset/liability. Decentralized finance, DeFi, is a financial system built on distributed ledger technology (DLT or blockchain) that allows a user to transact and own without an intermediary or centralized principal.

DeFi is in its early days, so the exact outline of such a system and the benefits achievable is not yet certain, but increasingly investors, including incumbent financial institutions, are building DeFi POCs and operations. Key DeFi applications being developed include decentralized:

• Payments: cryptocurrency and central bank currencies (CBDC)
• Lending (peer-to-peer)
• Exchanges (digital assets including securities, financial instruments, and currencies)
• Contracts (trade finance)  
• Digital assets and Non-Fungible Tokens (NFT).

Interest from financial institutions is growing rapidly, with over 80% of central banks considering the establishment of digital currencies and over 7k cryptocurrencies now in existence. The key question is: how can financial institutions explore and execute a strategy for building a DeFi set of offerings without diverting large sums from their existing business and at the same time reinventing the wheel?     

Capgemini’s framework and approach

To address this emerging opportunity with its clients, Capgemini has developed a set of assets and capabilities. Key components of Capgemini’s DeFi capabilities include:

• Tech Radar: evaluates and provides a POV on emerging technologies. Currently, 300 items have been assessed for the DeFi domain
• Digital Asset offerings: integration services for the digital asset custody lifecycle
• Trusted Data Exchange: an accelerator that enables data exchange for blockchain technology
• Blockchain Garage: consultants who work with clients to identify use cases, develop POCs, or integrate blockchain technologies into legacy platforms
• Ecosystem partnerships: a partner ecosystem of product and platform vendors
• Academic research: applied research programs with academic institutions
• Thought leadership: Capgemini published thought leadership pieces to identify trends and Capgemini’s POV on DeFi opportunities and challenges.

Both the opportunity and the challenges for DeFi are very large. To make an impact on commercializing this emerging technology, Capgemini has decided to focus its efforts in two areas:

• Orchestrator for CBDC products: central bank digital currencies can provide payment capability to consumers who currently do not have access to the financial system (financial inclusion). It can also reduce the cost of cross-border transactions. Because digital currencies can embed code, they are programable to promote governmental goals. These currencies will need custodians to reduce consumer risk. This will likely be retail banks that will store the currency in digital wallets for consumers. Capgemini is building the capabilities and running the POCs with banks to support orchestrating and managing CDFCs. Examples of engagements to date include:
  • Working with SWIFT to build CBDC integrations to enable cross-border, cross-digital currency payments
  • Developed reference implementation of a decentralized crypto exchange
  • Built a distributed trade finance platform for a trade finance industry participant
  • Developed payments infrastructure for multiple central banks    
• Digital asset custody: digital asset custody is necessary to address the issues of asset security, theft, and system availability. Large retail banks typically have internal teams working on setting up digital asset custody services. Capgemini is targeting all other types of retail banks with services including Consulting, Implementation and Operate. Capgemini has domain-specific custody IP in asset tokenization, asset transfer networks, digital wallets, on-chain settlements, security, lending, and reporting. Because time-to-market is critical in this fast-evolving market, middle-market banks are looking for pre-packaged solutions that can be configured and rapidly deployed.

Conclusions

The financial services industry currently operates on a highly centralized operating model. The model works well, but the centralized model has high cost and complexity. Cost and complexity effectively limit access to the industry to customers, vendors, and products that already have large financial resources. DeFi operates on a decentralized model using DLT technology to deliver service without intermediaries or centralized principals. DeFi offers the promise of lower cost and greater access for transacting and owning digital assets.   

The scale and speed of the transition to a DeFi business model are very high, which requires vendors and banks looking to succeed to focus on a few opportunities and specialize in building a competitive advantage. At the same time, any initiative needs to be plugged into an ecosystem of FinTech vendors to provide infrastructure and context to each bank’s initiatives.

Capgemini has built, and is growing, an ecosystem of FinTech vendors for the DeFi opportunity, and has chosen to focus on two opportunities in the DeFi space (orchestrator for CBDC products and digital asset custody).

These mutually reinforcing opportunities position Capgemini to provide infrastructure services (both implementation and management) for individual banks’ initiatives. These offerings will allow banks to focus their efforts on business model issues and the creation of differentiated offerings for their markets. By reducing time-to-market for new DeFi offerings, banks should be able to create new businesses and attract new customers who have never participated in the financial services industry previously.  

In NelsonHall’s newly published market assessment, Digital Banking Services: Transforming the Financial Services Industry, we found that financial institutions are changing their approach to digital services: from a focus on digital channels and CX to a focus on rapid solution development, cloud migration, data management, and STP.

The goal for financial institutions today is to be able to rapidly iterate new business models in an open banking environment. The pace of industry change is increasing, while labor-based operations are slowing financial institutions’ abilities to respond. The pandemic has accelerated the pace and scope of change as all business functions have had to move to remote delivery.   

The state of digital services in banking

For the past year, financial institutions have been:

• Expanding the use of automated software development tools, low-code/no-code techniques, and APIs to enable platform modularization, new functionality, and the move to the cloud
• Standardizing data management and orchestration across silos  
• Accelerating the use of cloud delivery and BPaaS.  

Over the next year, financial institutions will focus on:

• Testing and running open banking environments to start building an ecosystem of participants and a library of business models
• Increasing their focus on human/bot teams and their effectiveness
• Reducing time-to-market for new product offerings and increasing the number of new offerings
• Applying intelligent automation to more low-volume manual processes.

However, the external environment has put up barriers to transformation. The key barriers impeding the efforts of financial institutions include:

• Opening the core platform to external parties: open banking and new stakeholders requires development of APIs and plugins for legacy platforms to increase interoperability
• Enabling new digital business models: increasing platform agility and new functionality to support new businesses
• Building and enabling partnerships: sourcing IP from open source, acquisition, or shared products
• Working with new markets and products: different markets and products have different cost structures. Banks are shifting to high-volume/low-margin products (e.g., customers: mass affluent and unbanked; products: self-service and robot-advised). These offerings require high automation, embedded AI, multi-channel access, and high compliance capabilities, each adapted to multiple, unique markets.

Rising to the challenge

To address these challenges successfully, financial institutions need to focus on two activities: strategy and execution.

Key factors in strategy include:

• Changing the business model: banks need to reduce fixed costs and be able to scale volumes at a constant margin to be able to rapidly change business models across time and markets 
• Developing a roadmap to achieve agile/flexible operations delivery using cloud/BPS/heterogenous delivery
• Building an ecosystem of operations vendors with domain knowledge and experience with clients’ operations environment, vendors with complementary digital skills to deliver services, and the ability to work within client operational practices and transfer knowledge.

Key factors in execution include:

• Working with hyperscalers: migrating internal operations by standardization, consolidation, and modularization of platforms
• Redefining the external/internal operations split (with the rollout based on tested use cases):
  • External processes: high value, non-repetitive, cyclical processes
  • Internal processes: lower value/less differentiation/less volatile
• Selecting emerging product vendors for functionality, roadmap, financial strength, and the product vendor ecosystem. Preferred vendors should have the widest pool of IT services providers supporting them
• Orchestration: selection and implementation of orchestration tools to manage a heterogeneous system of vendors and products.

In summary, financial institutions are changing their goals from improving process efficiency for static businesses to increasing operational agility to continuously changing business models and operations. The operational changes being made will drive business model change. And these operational changes will drive accelerating change in banks’ product offerings, customer base, and market presence.

Find out more about NelsonHall’s “Digital Banking Services: Transforming the Financial Services Industry” market assessment and forecast report here or contact Guy Saunders. 

In 2022, the financial industry will continue to focus on building out its cloud and digital services infrastructure. This will be a deepening of capabilities for banks that have started their transformation, and the start of the journey for institutions that have not yet initiated their transformation. The growth rate of digital transformation is very high at ~20%.

Since most technology services vendors now derive 40% to 60% of their revenues from digital transformation services, this high growth rate will build on a large revenue base. However, there are two areas that have small revenues but much higher anticipated growth rates. We predict that these areas will start to make an impact in 2022, and be the growth engine for IT services in the financial industry in three to five years. They are:

• Delivering open banking processes in an industrialized environment
• Scaling neo bank delivery. 

Our discussions with bank executives indicate that these are high priorities, though they also highlight a lack of consensus, with a wide range of perceptions. However, it is clear that execution will need third-party vendors to support and provide rapid, effective, and scaled services.    

Industrializing Open Banking Delivery

Open banking has been a regulatory initiative for five years, but is only now moving to the execution stage in a few markets. Advanced markets moving ahead today include the Nordics and Benelux. These smaller markets make experimentation more feasible for a bank to launch a full-scale open banking initiative, and it is also easier to reengineer operations on the fly based on customer feedback. And, in smaller markets, it is easier for the financial institution to design an offering for a new customer demographic and vet partners to deliver a quality customer experience.

Delivering successful customer experiences in the initial open banking initiatives will drive customer awareness and willingness to adopt new offerings. Currently some Nordic banks are pursuing initiatives (e.g. in small business markets) which, if successful, will enable them to capture very large market shares of new customer demographics. The key is these markets are all domestic. It is rare that a bank in a mature market can pursue a domestic market opportunity as large as its existing markets.       

Scaling Neo Bank Operations

Startup digital banks (neo banks) focus on their business model and the design and creation of new banking products. Ongoing operational delivery is not considered to be a core competency. Neo banks have been around for five plus years, but many have failed and just a few, such as Marcus, have grown to considerable size. None to date have become a tier one bank or financial services provider. Each market has seen different types of startup financial institutions opened. In the U.S., payments vendors have predominated; in Europe, digital banks have been the predominant form of startup. 

The financial institutions (i.e. banks, payments processors, lenders such as Marcus, Revolut, Stripe, or Square) that have grown since founding must now scale up operationally to drive home their digital advantage before competitors match their digital value proposition. Scaling their business does not just mean scaling transaction processing, it requires scaling control, compliance, and security. Third-party BPS vendors are required to support this transition to large-scale, multi-product, multi-market operational delivery. Vendors that can provide combined domain-specific ITS and BPS services are rare. In 2021, BPS services grew slowly as clients focused on ITS for digital transformation. In 2022, BPS services focused on supporting neo bank growth will provide most of the growth in banking BPS revenues.  

Summary

Financial institutions have been experimenting with new business models and customer methods. In 2022, these initiatives will need to scale the business, not just the transactions (which hyperscalers have been doing). Delivering domain-relevant operations at high scale will become an emerging focus of the digital financial industry for the next five years and drive large revenues for vendors who can deliver these comprehensive services. 

On December 15, 2021, Kyndryl and Viewpointe announced a cloud migration, modernization and management contract. The engagement provides insight for BFS executives into how banking services operations vendors are moving to the cloud to simplify operational delivery and enable business model change to expand and improve their business.   

Current state

Viewpointe provides content management services for its owners, leading U.S. banks that include Bank of America, Truist, U.S. Bank, and Wells Fargo.

Viewpointe also delivers services for banks that are not part of its ownership structure. It delivers its services from two data centers (operating as primary and backup) in the U.S. It employs a proprietary platform to deliver services, which are heavily customized for each client. The primary content stored and managed by Viewpointe is mortgage/loan and checks/payment documents. Banks and their customers can search, access, and exchange content. Viewpointe develops and maintains the software platform. Kyndryl has been providing infrastructure orchestration and management services to Viewpointe since its inception in 2000.

Each client requires customization of the content management software to deliver a differentiated customer experience for each bank client. This has meant that each solution instance and sub-environment is unique and monolithic, on top of fixed infrastructure.

The engagement

Viewpointe wants to transform its application portfolio into a microservices delivered platform where:

• Clients can customize their platform, but platform management can be applied in a standardized fashion (e.g., patches, updates, management, and security will be rolled out rapidly with standardized solutions)
• Environment management will be handled by third parties (i.e., cloud delivery and environment orchestration)
• Viewpointe can develop new functionality and easily roll it out across heterogeneous client instances
• The entire environment can scale rapidly for new clients, markets, and product lines.

To achieve these goals, Viewpointe and Kyndryl have agreed to an engagement with steps including:

• Lift and shift to Azure cloud:
  • Kyndryl and Azure design a landing zone to take the Viewpointe platform from its on-premise centers to the Azure cloud
  • Kyndryl moves the platform to the Azure cloud, setting up orchestration and management services to manage the infrastructure environment
  • Kyndryl and Azure partner to adapt to the changing cloud environment daily using best practices  
• Transform the platform:  Viewpointe implements solution changes to introduce new product lines and/or client feature customizations 
• Scale the engagement: currently, client operations supported are all based in the U.S. In the future, Viewpointe expects to adapt its platform and delivery to support clients in additional markets with additional product lines and functionalities.

The journey will take three years, including:

• Year 1: migration to Azure cloud with new cloud-based orchestration and management capabilities implemented by Kyndryl
• Years 2-3: Viewpointe rebuilds the content management platform to fully adopt a microservices approach.

The engagement is expected to result in cost savings of 20% to 30%. 

Conclusions and benefits

Cloud migration has been effective for highly seasonal or cyclical businesses, where volumes spike and the client only pays for capacity as it is used. Static workloads typically cost more in a cloud environment. This engagement highlights a use case where cloud delivery is more cost-effective than internal delivery. Features that must be present to make the case for cloud include:

• Heterogeneous environment by client: each member of the client base has high levels of platform customization resulting in each instance being materially different from all other instances
• Expanding the set of product offerings: rapidly expanding product offerings requires the client to deploy new solutions into the environment frequently. These solutions must rely on core functionality in the existing platform to deliver service 
• Expanding markets: rolling out support for clients in additional markets that can be more easily supported from a Hyperscaler’s data center network
• Highly regulated industry: security and compliance requirements in highly regulated industries are frequently updated. A standardized microservices environment allows clients to rollout standardized updates across the entire environment faster, with greater accuracy.

Ultimately, the case for the cloud is that it allows Viewpointe to accelerate new product launches and standardize its operational delivery, while still enabling its customers to customize their platform instance.

Digital startup banks are built on FinTech functionality to deliver financial services in an omnichannel environment. There are two types of FinTechs: B2B businesses that have a proprietary platform to deliver service to clients, and B2C businesses that provide clients with an all-digital banking experience.

WNS: expanded focus on B2C FinTechs

While WNS supports B2B and B2C, it has seen more traction in the latter, and has expanded its services to support the B2C FinTechs. The key market segments include:

• Digital banks
• Personal finance
• Wealth managers
• Business finance (small businesses)
• Payment providers/digital wallets/crypto firms.

Because the B2C FinTechs work with consumers, they require support for compliance and customer support, and these processes require human interaction to succeed and grow. Only 10% of FinTechs survive, but successful firms require massive scaling to meet customer service requirements. The good news is digital banks have lower exception rates (typically a 3% exception rate, versus 10% from a typical tier one bank) because their core processes are all digital.     

However, many of these startup digital banks have faced operational challenges. Traditional banks are retaining customers because they offer the entire range of banking services, at scale, which encourages customers to maintain their business with them. FinTech banks that have faced challenges include Tesco Bank which has sold off its mortgage business and is closing its clients' demand-deposit accounts. Similarly, M&S Bank, a JV of M&S Stores and HSBC, has closed its in-store branches and is also closing its customers’ demand-deposit accounts. The lesson is that full product-line operational delivery at scale economics is critical to success.

WNS services for startup FinTechs

WNS has pursued the startup B2C FinTech market with a three-pronged set of services:

• Speed to startup:
  • Map regulatory requirements
  • Identify volume and staffing requirements
  • Establish controls and SOPs
  • Establish risk management, compliance, and control frameworks
• Scaling growth for individual processes:
  • Institutionalize best practices
  • Scale staffing to match business potential
  • Performance management
  • Automate repetitive manual processes (e.g., exception management)
• Scaling growth across multiple processes and improving the customer journey:
  • Scale multi-geography operations
  • Incorporate new regulatory and process requirements into frameworks
  • Implement IA, AI, and omnichannel CX.

WNS engagements with FinTechs add processes over time, and the typical progression of services includes:

• KYC/AML/Fraud and compliance: most engagements start with this set of processes
• CX and onboarding: engagements next add in compliant onboarding services to achieve higher conversion rates, faster overall growth rates, and improved CSAT
• Back office services: reporting, accounting, fulfillment.

WNS can enhance its clients’ capabilities with functionality from its own set of tools or its ecosystem of FinTech solution providers which delivers emerging functionality in four key areas:

• Customer service
• Origination and servicing
• Analytics
• Compliance.

By using this rollout strategy and set of offerings, WNS has been able to scale individual engagements by 2X to 10X in one year. Overall, WNS has been able to grow its digital startup bank business this past twelve months by 40% y/y. Clients using these services have experienced a 30% reduction in TAT, 95% improvement in CX, and 30% reduction in application processing times.

Conclusions

Digital startup financial institutions are looking to bring new business models to market quickly. Third-party services vendors need to provide STP and a comprehensive set of services to support these emerging BFS services providers. WNS has built a set of operational services which enable startup banks to scale fast and continuously deliver new functionality to customers. This enables digital startups to attract and retain customers by delivering differentiated value. In contrast, tier one banks look for vendors to deliver siloed functionality.

Startup banks need a vendor that can deliver broad operational support. Sourcing, organizing, and managing resources is a complex challenge for a bank. WNS has pulled together the relevant components and frameworks necessary to deliver a full-service operations environment. As startup banks and local banks look to grow their businesses, they will increasingly rely on third-party vendors for this type of support.  

Banks are accelerating their move to the cloud to respond to the pandemic, enable greater operational agility to reduce time to market, and develop open banking capabilities. At the same time, performance dispersion (the operational variance across institutions within the industry) has increased significantly.

Cloud delivery can change operational performance and hyperscale businesses. So, how can banks move their operations to the cloud most effectively, and what tools and frameworks are needed to best adapt cloud operations over time? Infosys has developed a set of tools and frameworks, Cobalt, to address these challenges.

Infosys’ Cobalt Offering

Cobalt, Infosys’ cloud framework offering, is designed to address the key challenges enterprises face when trying to move their operations to the cloud. These challenges include:

• Security: the ubiquitous challenge of the cloud is providing satisfactory cybersecurity   
• Modernizing and innovating their platforms: enterprises are moving to the cloud to modernize their platforms, but access to a broad range of tools and applications in this rapidly emerging market remains a challenge. Maintaining flexibility requires enterprises to avoid vendor lock-in
• Improving speed to market: implementing new solutions quickly and effectively in a multi-cloud environment is subject to wide variation across domains and companies. Access to talent continues to be a gating factor.  

The Cobalt offering provides a solution across these five key areas of accelerated cloud adoption by any enterprise:

• Mainframe modernization: tools for assessment, rules extraction, and component migration 
• Cloud-native development: tools for the development of cloud-native apps
• Database migration: database migration frameworks and tools
• Migration: frameworks and tools for migrating apps to the cloud
• DevOps: tools and frameworks to accelerate the adoption of DevOps across the bank.

Infosys believes cloud migration requires industry-specific IP to fully take advantage of the cloud’s benefits. Infosys has created a financial services-specific Cobalt offering, FS.Live.Cloud, that includes, but is not limited to:

• Open Banking solution: an open banking API platform
• Recon-in-a-box: recon platform to manage operational risk and provide controls
• Mortgage-as-a-service
• Business banking: Virtual CFO
• AML/KYC
• Media platform: digital asset and content management
• Voice-based solution: voice-based solutions integrated into Alexa
• Location-based solution: enhanced CX with value-added services based on location.

This packaged version of FS.Cloud.Live helps mid-size financial institutions with a cloud solution at an overall cost of 15% to 18% lower than buying public cloud services direct. In addition to the services described above, this offering will:

• Host applications from Infosys and third parties (e.g., Actimize, Calypso, Avaloq, and Fiserv)
• Provide clients with the ability to build new cloud-native platforms using accelerators for cloud-native development.

Large financial institutions in most cases have either developed or are developing a comprehensive cloud strategy and they can benefit from leveraging components of FS.Cloud.Live to optimize migration times by 30-40% and deliver a much lower overall TCO.  

Conclusions

Financial institutions of all types are looking to migrate to the cloud with the help of third-party services and technology vendors. Sourcing, organizing, and managing resources is a complex challenge for any financial institution. Infosys has pulled together the relevant components and frameworks necessary to deliver a cloud migration project, with ongoing environment management updates, to enable regional and local banks to capitalize on the cloud opportunity. These services are useable by line-of-business executives, not just technologists, which allows the LOB to directly adapt the capabilities to align with their business objectives.   

As banks migrate more of their operational footprint to a multi-cloud environment, the technology will become more robust. However, the business advantage comes from being an early mover. All financial institutions are looking for the broadest, most robust set of tools, technological and human, to enable them to transform their business models for a more agile, open banking industry. The FS.Cloud.Live platform is a good example of the type of tools banks need to start their cloud journey.

The pandemic has accelerated the adoption of digital transformation across all industries, and in the financial sector, operational transformation has grabbed the top spot in the priority list for investment and spending. At the same time, performance dispersion (the variance across institutions within the industry) has significantly increased.

So, how can banks make their transformation initiatives effective, and what drives performance? Multiple firms, banks, and IT/operations services vendors, are now creating digital transformation frameworks to help improve the effectiveness of these efforts.

Capgemini’s TechnoVision framework

Capgemini has created its own transformation framework, TechnoVision, which identifies key business/technology levers, evaluates technologies, enables initiative prioritization, and supports disciplined creation and execution of a transformation strategy.

Strategic considerations are based on three core principles, backed by technology domains, which are:

• Standards: each technology, and the domain it is applied in, requires customized standards which are set by the ecosystem it operates in
• Data: sourcing, scrubbing, analyzing, and using data in businesses
• Security: security practices relevant to the specific operational environment.  

Technology domains under consideration are Cloud, AI, Immersive, Connected, High Performance & Decentralized Technologies. The operational framework is adapted to each industry where it is employed. TechnoVision has the following key components:

• Infrastructure areas for transformation:
  • Invisible infostructure: coordination of the technology infrastructure  
  • Applications unleashed: the application and API domain  
  • Thriving on data: the data and AI domain
  • Process on the fly: the process automation domain  
• People-focused areas:
  • You experience: stakeholder experience domain  
  • We collaborate: workforce and partner coordination  
• The design principles for transforming delivery technology. Here, the principle of balance by design aims to balance and coordinate people and systems to create sustainable delivery.

The framework allows banks to move from a centrally controlled organization (hub and spoke model) to a decentralized model, coordinated by a set of operational/business standards and a single golden-source set of data. The value of the framework is that it can deliver:

• Growth: driven by hyper scaling in two directions: matching costs/revenues, and the ability to harvest smaller economic opportunities more efficiently
• Efficiency: driven by lower error rates, reduced reconciliation/fixes, improved STP/fulfillment, and lower cost
• Stakeholder engagement: driven by higher CSAT, improved CX, higher up/cross-sell
• Resiliency (losses previously to generational change, silos, and latency responses to societal changes)  

Conclusions

Financial institutions of a wide variety of backgrounds and characteristics are looking to undertake digital transformation with third-party services and technology vendors. There needs to be a meeting of the minds among all stakeholders to ensure the transformation process is effective. A transformation framework helps to establish and communicate a common set of goals, understandings, and roadmaps across stakeholders.

Capgemini’s TechnoVision framework for financial services helps banks address transformation issues across the enterprise, including standards, data, and security. The framework has been used in many global and regional banks to define and accelerate the transformation journey. Other services vendors have similar frameworks, though TechnoVision is distinctive in its use of sessions with local banking executives to create localized banking roadmaps which are not reliant on a hub and spoke digitalization architecture. We expect to see Capgemini build more real-time transaction capabilities with its clients to address banking’s move away from wide settlement windows and batch processing.

NelsonHall recently published a market assessment and forecast report on Intelligent Automation in Banking: Transforming Operations. We found that financial institutions must automate their operations if they are to compete successfully in a rapidly evolving marketplace. Currently, they operate with manual processes using a heterogeneous set of platforms, acquired over many years of M&A. However, the pace of industry change is increasing, while labor-based operations are slowing financial institutions’ abilities to respond. Cognitive, AI and RPA technologies are allowing these institutions to become more agile, cutting the cord from labor-based value-add, without having to do a rip-and-replace of their existing platforms.

The state of IA in financial services

For the past year, financial institutions have been:

• Expanding the use of process discovery to identify new targets
• Standardizing processes across silos  
• Focusing on the use of IA for employees for WFH and fieldwork (especially in response to COVID-19)
• Accelerating their delivery from the cloud   
• Adopting the use of hybrid AI/RPA to support agents and advisors.

Over the next year, financial institutions will focus on:

• Managing data across silos and markets
• Using agile techniques to deploy new functionality with DevOps and Lo/No code solutions
• Increasing their focus on human/bot teams and their effectiveness
• Applying IA to low-volume manual processes.

However, the external environment has put up barriers to transformation. The key barriers impeding the efforts of financial institutions include:

• Access to emerging technology: all IA services vendors are building ecosystems for emerging technologies and acquiring staff skilled in relevant technologies. Finding the best new technologies and embedding them in effective platforms remains difficult     
• Access to qualified staff: changing technologies changes the required mix of staff skills, and emerging tech is driving a skills shortage. Currently, AI is the most constrained capability  
• Changing regulations requiring resources to adapt to them
• The COVID-19 induced move to more distributed operations:
  • The need to reduce supply chain risk by increasing the distribution of operations 
  • Increased use of joint human/bot work teams, and how to make them effective
  • Shifting delivery to the omnichannel environment and rearchitecting processes   
• Changing business models: the advent of digital banks (e.g., Marcus) and the need to create new competitive business models to address this challenge.

Rising to the challenge

To address these challenges successfully, financial institutions need to focus on two activities: vendor selection and execution.

Key factors in vendor selection include:

• Preferred product vendors should have the widest pool of IT services providers supporting them
• Focus on services vendors with skills in process discovery, breadth of solution partnerships, willingness to invest in operations, AI, domain knowledge, and data management capabilities  
• Building an ecosystem of vendors with knowledge of client’s business issues; complementary skills to build and deliver IA services;, and the ability to work within client operational practices
• Changing the operational model: banks need to shift from operational leverage (scale economies) to leveraging flexibility (ability to cost-effectively switch out workloads).

Key factors in execution include:

• Preferred vendors should have the widest pool of solution providers supporting them
• Redefining what is delivered by third parties, internal, and cloud to maximize flexibility and speed new intelligence and automation 
• Focusing on increasing the yield of use cases by focusing on KPIs for successful use cases 
• Transforming application development to a DevOps model to speed the innovation cycle.

In summary, financial institutions are changing their goals in two important ways:

• From improving process efficiency to improving data management
• From improving cost efficiency for static businesses to agility for continuously changing businesses.

The operational changes being made will drive business model change. And these operational changes will drive accelerating change in banks’ product offerings, customer base, and market presence. 

Find out more about NelsonHall’s Intelligent Automation in Banking: Transforming Operations market assessment and forecast report here or contact Guy Saunders.

Banks will spend 2021 pursuing different aspects of three key initiatives that have become more important since the advent of COVID-19. The first is process automation, which has become more important but needs to increase its delivery effectiveness.  The second, work-from-home (WFH) has also accelerated under COVID-19 but will need to change in 2021 to continue to be effective. The third is the application of cognitive to processing. Cognitive is an immature technology and has had teething pains with bias and ethics. Users of cognitive technologies will need to include controls to meet ethics requirements that society requires of its business community.  

Process Automation

Process automation has been an accelerating trend for the past four years. Since COVID-19 struck earlier this year, the adoption rate of automation has pulled three years of anticipated adoption work into the second half of 2020. However, this acceleration has highlighted key challenges to effectiveness. They are:

• Process discovery: the acceleration in the pace of adoption has highlighted that banks do not fully understand the processes and process bottlenecks that need automation. Multiple vendors have been developing enhanced process discovery tools to increase the speed at which banks can identify processes   
• Use case yield: most banks have been toying with many POCs to try to improve process effectiveness. However, the majority of cases (sometimes as high as 90%) fail to meet their required rate of return to justify their deployment. Third-party vendors are well-positioned to improve the yield on POCs due to their work with multiple clients in many industries. Increased rigor of use case development, based on experiences to date, will enable higher throughput from conception to operations 
• Orchestration: once the bots are deployed, especially in a highly distributed environment such as cloud or multi-country, effectiveness declines rapidly. Orchestration tools are being built and improved to improve effectiveness.

These issues have become a priority for banks. Our conversations with bank executives indicate their highest priorities for RPA engagements are:

• Increasing the yield to operational deployment from POCs and use cases
• Maintaining the effectiveness of bot deployments while adapting the operational environment to changing business conditions.

In 2021, they will focus their automation initiatives on compliance (especially the conversion of loan contracts to a post-LIBOR world), customer service, improving agent effectiveness, and KYC/AML reviews.

Work from Home

The COVID-19 world has seen a mass migration to WFH. However, CEOs and other leaders are concerned about workforce morale and training issues over the long term. Bankers we have spoken to expect WFH will remain a larger part of the financial services environment, but much smaller than it is today. Specifically, interviews we have conducted indicate banks expect 70% of workers to move back to offices and ~30% to remain in a WFH environment. Over the next year, institutions will have to work out what the shape of the new workforce deployment will be. Key issues will include:

• Which workers will be on-premise and which in a WFH environment?
• What are the criteria for working in each environment?
• What are the conditions and frequency of being on-campus or off-campus?
• How will this impact the shape of teams?
• What automation and security will be required to support workers over the long term in this new environment?

Until the lockdowns end there will not be much movement on this issue. However, the second half of the year should see the economy open up and these issues will take center stage.  

Cognitive Processing Support

Banks and technology vendors have been developing and trialing cognitive support tools for many years now. The aggressive move to digital channel environments caused by COVID-19 has both enabled the application of cognitive tools to business processes and necessitated the use of cognitive tools to deliver services. However, accelerated deployment of cognitive tools has highlighted challenges including:

• Bias: ML and AI tools are inherently backward-looking. Improving society or reducing inequities will not happen from a backward-looking analysis. The algorithms, use cases, and human oversight of these tools will be overhauled in the upcoming year
• Compliance: to date, the primary use of cognitive has been within an individual market and silo at a bank. Moving into 2021, banks are looking to deploy cognitive across silos and markets. Understanding data from across heterogeneous environments is a big challenge, but regulators are requiring banks to deliver on this. 

The above growth areas of technology services for banks in 2021 are the result of 2020’s rapid deployment of immature technologies. Each of the three growth segments of services will, as usage matures, provide banks with increased agility to adapt to a rapidly changing business environment.

I recently participated in a webinar panel during the Consumer Banking Association conference with Genpact and TD Bank. The panel topic was Genpact: Turning Crisis into Opportunity for Consumer Banking, and it addressed three core topics:

• The global health and economic crises have reshaped consumer banking
• Banks are adjusting to serve customers digitally while trying to maintain empathy and human connection
• Banks can accelerate and prioritize their digital investments to drive real change.

The COVID-19 crisis has accelerated previous trends towards digital adoption, including:

• Cloud migration, as banks have moved from a single cloud strategy to a hybrid, multi-cloud strategy
• Omnichannel migration, as banks close branches and expand online alternatives
• Cybersecurity adoption, as banks seek to secure their data and operations in the cloud.

TD Bank has found that banks have moved at light speed to implement the PPP Act and financial relief offers for customers. TD Bank’s relief offer, TD Cares, was built with three concepts in mind:

• What benefits do stressed customers need?
• What products can best deliver these benefits?
• Outreach to customers the bank anticipated might need support. The guiding principle is meeting the customer where they are (online, in-person, etc.).

The delivery of these services was developed with a “digital-first” approach, followed immediately with human interaction to support robust interaction and CSAT.

Overall, banks are trying to prioritize where they will be spending their money. When a bank looks at digitizing a process, such as onboarding or payments, where in the value chain should it start and focus their investment dollars to make the largest operational impact? The key to succeeding in delivering value is to optimize the customer experience. To date, banks have looked at improving their processes. However, customers are comparing their online experiences against their experiences form other industries, such as the consumer products or entertainment industries.

COVID-19 has forced banks to rethink how and when they engage customers. Key examples of changes made include:

• Much faster decision-making process (e.g., for lending). Digitalizing applications and providing STP to decisioning algorithms has enabled closer to real-time approvals for standard borrowing requests
• Real-time digital channels: by deploying armies of chatbots, AI, and decision trees; banks have been enabling employees to engage across all channels with customers while knowing who the customer is in real-time.

The banks are not just trying to improve efficiency, they are trying to create empathetic, differentiated experiences in their customer interactions. Creating a value chain that delivers digital, empathetic, differentiated customer experiences will require a synthesis of bank operations, data management, and FinTech partner ecosystems. By integrating digital processing, bank/customer data, and new functionality from an ecosystem of vendors, banks should be able to know each customer and create a custom experience for that customer. The industry is in the early stages of creating such customized experiences, but the journey has begun. For example, a bank has been able to deliver empathetic, customized experiences by digitalizing customer contact apps across silos. This requires all stakeholders to participate to move a project ahead both thoughtfully and at speed.

Banks are not just building customer contact experiences with digital technologies. The panel identified payments as an area of rapid transformation, where customers experience the use of the product itself as a consumer experience. There are two areas within payments that are a focus for innovation today:

• Payment initiation: identifying the payor at the POS to provide a seamless, real-time payment experience
• Post-payment services: delivery of services such as credit extension and loyalty program assignment.   

Again, modularizing the process with support from an ecosystem of specialized vendors is enabling payment companies to restructure the industry and improve customer experience.

The key takeaways from the webinar panel are that, because of COVID-19, banks are moving faster than ever to digitalize their businesses. The digitalization of business does not eliminate the human element but rather increases empathy and human engagement to drive a differentiated experience. Because the canvas is large, banks are challenged to focus their investments in high-impact areas. The areas where banks are finding success are in digitalizing processes that draw data and resources from across silos to deliver faster, customized experiences for customers.   

The past decade since the global financial crisis (GFC) has been good for the lending industry. Loan delinquencies in the U.S. reached their highest level after the GFC in the first quarter of 2010 at 7.4% of commercial bank loan portfolios. Since then, loan delinquencies have fallen to 1.44% of loan portfolios as of the fourth quarter of 2019.  At the same time, loan portfolios have grown 26.8% larger than they were in the second quarter of 2013. During this period, banks and lenders have reduced collections staff as delinquencies have declined. Banks can support delinquency collections if portfolios maintain low default rates.

However, COVID-19 has had a major adverse impact on the economy. During this time unemployment rates have surged and GDP forecasts have plummeted.

This type of economic contraction will aggressively drive up collection activities at banks. But scaling collections activities will be challenging when all lenders will be trying to scale-up their activities at the same time, and as new regulations constraining collection methodologies are being issued.

The automation and operating framework will be critical to successfully navigating the collection environment. I recently participated in a Consumer Bankers Association hosted webinar with Genpact where they outlined their approach to improving collections services. Genpact has developed a three-part methodology for transforming their clients’ collection operations:

• Immediate response: creation and deployment of rapid response teams with simplified decision-making structures to address emerging issues and deteriorating portfolios
• Adaptive workforce: reskilling workforces quickly and shifting delivery venues to work-from-home (WFH) or other remote options
• Strategic capabilities: investing in digitalizing collections processes and embedding cognitive and self-serve functionalities 

The challenge is large, so success requires a set of prioritized actions to make early gains possible. Examples from Genpact include:

• Immediate response measures taken to date include efforts to proactively identify and remediate high-risk customers before they hit delinquency. Clients can reduce exposure related to high-risk customers by leveraging machine learning to develop and deploy a combination of credit limit decrease and blocking, or proactively offering customers hardship plans. Using text-mining of agent notes from customer service interactions has allowed early identification and treatment of high-risk customers
• Adaptive workforce measures have had the highest activity to date, and include:  
  • Rapid, remotely delivered workforce training. For example, Genpact is helping a top 5 global bank is ramping up their operations from 600 to 1600 skilled collections resources
  • Workforce multiplier support such as a group of rapid response teams (cross-functional teams that can address the rapidly evolving customer dynamics through agile pods). These teams deliver services such as surge-capacity hiring, training, and deployment all with WFH solutions
  • New models of data usage, both new data forms, and alternative data streams. COVID-19 has changed customer behavior, and ML needs to be used to identify new customer segmentation patterns and the appropriate response. This is not a one-time activity; it requires weekly refreshes as the data/behavior changes
  • Regulatory changes such as the Consumer Financial Protection Bureau’s recently published new contact policies. Straddling compliance and good practice is the need to define and test what empathetic response should be. There are no clear metrics, particularly when it comes to a customer situation where they may be impacted economically, socially, and health-wise
• Strategic measures taken to date include investments in:  
  • Channels of customer contact and payment beyond the voice channel, including chatbots and self-service
  • Automation of manual back-office processes in collections using RPA or workflow solutions, so that agents can focus on customer management. This has been especially useful for implementing the CARES Act program
  • Use of AI, ML to develop and offer unique hyper-personalized treatment strategies suited to specific situations and requirements of individual customers. These customized solutions can reduce customer hardship as it considers the individual realities to make the debt more manageable for customers
  • Building empathy into conversational tools: for example, real-time speech analytics and conversational AI to prompt effective payment plan matching to customer needs using the appropriate verbiage usage based on customer talk

In short, scaling the same processes, under conditions of reduced resources, increased costs, and growing transaction numbers will not work. Already vendors are addressing these challenges by employing combinations of proactive outreach, workforce training, and technology. Lenders will then be able to increase their effectiveness and scale in collections to meet the rapidly increasing scale of operational delivery required.

To find out more on this topic, view the Consumer Bankers Association hosted webinar with Genpact and NelsonHall here.

This is an update on my last blog on the impact of COVID-19 on the financial services industry. Since then, I have interviewed many more industry executives both at banks and at operations services vendors. Industry responses are still muted, but clear signs are emerging that banks will be focused on prioritizing those activities which maintain continuity and support adaptation in an operating environment with larger volume swings.

Key transformation initiatives where banks are accelerating investments include:

• Remote delivery focused on workforce engagement and a relatively reduced effort in remote customer engagement 
• Migration to cloud delivery, with its ability to manage large volume swings
• Data management initiatives, with a focus now on default management applications
• Consolidation of operation solutions and methodologies. These are longer-term initiatives, but much higher ranking for global institutions than before COVID. At the same time, banks are increasing their search for multiple supply sources.

Banks have reduced their activities focused on support for: 

• Marketing and sales campaigns: business growth which requires capital has been sidelined 
• Expansion of suppliers: banks are now focused on vendors with the strongest financial positions. 

Impact on banks to date

To date, the impact on retail banks and capital markets firms has been to move workforces to a work-from-home (WFH) environment. This has not been a hard initial transition for banks in markets with mature internet infrastructure; however, banks in markets with weak public internet infrastructures, such as some Asian markets and most of Africa, have faced significant challenges moving employees to a home environment.

Even within markets, the success of moving to WFH has varied as government policies have changed. In India, the nation went into lockdown on March 24 for 21 days. This was extended to May 3 on April 14. Initially, many delivery centers struggled to move workers to a WFH environment, given a limited number of laptops per worker and poor connectivity in some regions. Over time, bank operations have been able to obtain permission for critical processes to be delivered from centers, with dormitory and hotel housing provided for workers. Non-essential processes have continued to be delivered from home. This has led to worker utilization rates at the largest delivery centers moving from an initial capacity utilization rate of 20% in late March to 60% utilization in early April, to 90% utilization rate with essential work done in centers and large numbers of WFH workers.     

While operations delivery has rebounded,  bank executives we have interviewed expect their businesses to aggressively deteriorate in Q2 2020. Specifically, they expect sales to decline ~25%, costs to increase ~7%, and profits to decline ~45% in Q2 2020. Fortunately, their operations supplier contracts are adequate to support a 20% decline in volumes (and a 25% increase in volumes). No one is sure how long the impact on business will continue. Based on announcements by governments and universities in the past few days, this analyst expects the COVID-19 lockdowns to continue, at some level, for at least six months. The saving grace may be that the continuing shutdowns will be at progressively lower levels of restriction.

Banks have been asked by regulators to provide BCDR plans for themselves and their suppliers. These have been supplied. Of note is that private conversations indicate banks and suppliers are setting triage plans for who and what to focus resources on if the impact of COVID-19 worsens. If that happens, expect to see suppliers retaining service to their most important clients and banks cutting back on product lines (i.e. low margin and risk products) and reducing suppliers to financially stronger vendors.

Transformation projects in production have continued as planned. However, new projects have been stopped in anticipation of restarting the process when lockdowns are lifted. However, as profit levels fall, the focus on cost-cutting will increase. Banks will have to prioritize which projects to restart as they face reduced capital to invest in transformation. Currently, many banks are looking to restart RPA projects when they resume projects. Successful RPA projects can scale processing volumes with a smaller workforce. Because scalability has become so important, banks are looking to restart initiatives that enable scaling, such as RPA and cloud migration.     

Finally, bank product lines have been aggressively impacted. Lending, except for government support loan programs, has all but stopped in all countries. Payment volumes, especially cross-border payments, have plummeted by over 20%. Physical branches have been shut down for business. The fall in activities has reduced operational requirements, but at the cost of profits for banks and revenues for their services providers. While bank executives have not projected volumes beyond Q2 2020, the outlook is very weak for a turnaround during the remainder of this year. ITS and BPO vendors will have stable revenues from long-term contracts. However, these vendors will find that new contracts are few and far between. Some BPO vendors are expecting to grow their business at +20% (annual rate) by buying bank or service vendor captive operations. As bank and vendor liquidity becomes a concern to regulators and investors, there are now captive operations actively for sale. For the next year, successful BPO vendors will have an active M&A strategy in place.        

Conclusions

To summarize, banks have not yet changed their operational delivery activities with third-party vendors. They have reassessed their BCDR plans. Financial institutions have begun to see very substantial drop-offs in transactional activity, and they expect this to impact their revenues and profits starting in Q2 2020. The largely anticipated drop-off in revenues and profits will drive a reassessment of their services contracts to drive lower pricing and sale of operations. There will be a consolidation of vendors and pressure on pricing. The scale and scope of the transformation in sourcing arrangements will be driven by the length of the COVID lockdown. When the lockdown abates, banks will redouble their efforts in digital transformation to prepare for any future pandemics.

My next blog post will address the impact to date of COVID-19 on services vendors to the banking industry.  

Banks have been aggressively transforming their operations to a digital delivery model. It is well known that a key driver, across all industries, is the need to attract new, primarily young, customers who prefer omnichannel interaction with vendors and demand high-quality CX in their business interactions. Less well known are two key drivers that make speedy transformation imperative. They include:

• Open banking regulations: they are driving changes to the business model. A successful transition requires operational agility, efficiency, and accuracy to enable emerging models and openness to allow external participants
• Price compression: the need to increase revenues to offset price compression requires improved sales/marketing campaigns with analytics, improved coordination across silos, and faster time to market for new banking products.

Regulations and price compression are making transformation an urgent need. However, the back-end structure of banks, monolithic 30+ year-old legacy platforms, present transformation challenges. Key challenges include:

• Digital technologies: emerging vendor landscape requires significant ongoing due diligence efforts
• Accessing emerging technological capabilities requires deciding when to partner, use open source, acquire, or share products
• Cybersecurity: increasing use of cloud, shared, and open environments increase vulnerability to cyber threats 
• Open banking requires the ability to adopt new digital business models, both in services delivered and internally delivered ops
• Standardizing process execution: Agility to adapt standardized execution to new processes, ending obsolete processes and standing up new processes. Agility is created from the successful assembly of software modules as required
• Cloud/PaaS/systems integration/consolidation: Banks need to standardize platforms to drive STP. Banks have limited internal staff, requiring third-party support for new platforms and operational delivery.

To address these challenges, services vendors have focused on professional services for on-premises platforms for large banks and cloud-delivered platforms for startup and regional banks. Increasingly large banks are looking for hybrid cloud-delivered platforms and modules. Digital technologies have reduced the effectiveness of labor arbitrage strategies. Vendors are aggressively automating their own delivery to remain relevant. Cloud-delivered DevOps is increasingly in demand by all clients. Banks want support for analyzing transactions and entities (customers and counterparties) to drive greater analytic insight and capability development.

Over the next eighteen months, services vendors will:

• Expand and deepen their ecosystems of FinTech product vendors with a focus on AI and DevOps for customer acquisition
• Expand their cloud vendor ecosystems to support moving new workloads to the cloud
• Expand their API libraries to support new markets and the integration of banks’ vendors into their digital operations
• Enhance their tools’ ability to make predictive analyses, not just descriptive analyses
• Connect front-end customer engagement to back-end processing, enabling STP and rapid fulfillment
• Refine their use cases to create higher yield rates for POCs (an increase from the current rate of <60%).

Banks we have spoken with are accelerating their plans to move to digital operations, both transforming their legacy operations and starting new digital initiatives. Many “all-digital” startups at established banks have shut down as the initial use cases failed to meet their goals. The common underlying reason for failure is that digital banks do not run themselves; they require ongoing investment and attention in order to succeed. Similarly, cloud operations do not reduce the cost of delivery in the long run. Cloud operations increase flexibility, which allows banks to enter and exit markets and products with minimal cost to the business. Banks with a clear roadmap and determination to go all-in on digital will have the best chance at creating a successful digital business.

At its 2019 Engage Client Forum last week, NIIT Technologies explained how it is adapting to a continuously changing business and technology environment to deliver digital transformation for its clients.

At its 2019 Engage Client Forum, NIIT Technologies explained how it is adapting to a continuously changing business and technology environment to deliver digital transformation for its clients.

Four key strategies

NIIT Tech identified four key strategies it is pursuing:

• Hyperspecialize in key industries: NIIT Tech has chosen the Insurance, BFS, and Travel industries to focus on. Its teams have adapted their approach from bringing technology to clients to analyzing business problems and bringing in technology to solve a business challenge
• Involute: Tying together cognitive, data, automation/integration, and cloud to accelerate value creation. NIIT Tech believes the next phase of digital transformation in the industry will require stitching together the entire business process with cognitive and digital technology to eliminate bottlenecks which have reduced value creation in past industry reengineering engagements   
• Moving the center of gravity closer to the client: by onshoring increasingly, and setting up delivery centers specialized in key industry processes (e.g. the center in Boise, Idaho which specializes in digital integration and Pega services)  
• Building a partner ecosystem: clients, including tier ones, have indicated they have difficulty curating an effective technology partner ecosystem. NIIT Tech helps them by scanning the field in a small set of industry domains for solution vendors who offer a product which can be integrated into NIIT Tech client domains in a way that provides agility, lower cost, and digital functionality for the client. The key attribute is solutions that approach problem-solving from a business perspective rather than from a technology perspective. Present at the conference were four FinTech partners:
  • Steeleye: compliance technology and data analytics vendor
  • Fennech: treasury solution for allocating and reconciling bulk payment allocations  
  • Appii: employment background check and validation platform which uses blockchain for CV verification   
  • Duco: data management solution vendor for data normalization, migration, regulation, and reconciliation.

Banking sector focus

NIIT Tech is building proprietary IP to drive forward its digital services initiatives. Key to the banking industry is:

• Open-source software. Key examples include:
  • Infrastructure-as-a-code: NIIT Tech uses Terraform, which is an open source infrastructure as code software tool created by HashiCorp. It enables users to define and provision a data center infrastructure, and it works with most cloud provider environments, including AWS, Azure, Google, and IBM
  • Core banking platform: NIIT Tech uses BankingEasy,  a cloud and web-based, multi-user, multi-currency and real-time, modular, core banking platform. The platform runs on n-tier, service-oriented architecture on Microsoft Azure.
• Application performance management framework: this tool helps clients with heterogeneous legacy environments to manage application lifecycles more efficiently to improve application behavior in a complex operations environment by analyzing the monitored data for the entire relevant operations environment giving real-time performance visibility across the application development lifecycle. It also informs how the application performance is impacting a client’s business and identify areas which require immediate attention. The framework is powered using AI and automation capabilities, thus reducing manual efforts and enabling teams to focus on product innovation.
• Jumpstart analytics: NIIT Tech via its jumpstart analytical offerings, Maestro and Xpresssssss, helps clients modernize their data landscape by setting up data lakes, and migrate their legacy data warehouses to cloud technologies (AWS, Azure, GCP, etc.). Their data science library of pre-set use cases (e.g., credit scoring, fraud detection, customer/advisor churn, etc.) enable clients to realize business objectives via advanced data science techniques.  

The client view

During the conference, I spoke with many clients about their activities with NIIT Tech, providing the following feedback:

• NIIT Tech is starting U.S.-based training programs to support reskilling the client’s employees
• It is willing to co-invest with clients in projects by spending 2 to 4 weeks working on use case development without charging for the engagement
• NIIT Tech employees are moving from a labor-based delivery mindset to a business outcome-based mindset, which more closely matches the client’s approach to challenges.    Clients want vendors to proactively provide suggestions, which NIIT Tech is increasingly providing. NIIT Tech can provide both labor capacity and capability as required rapidly when they have spike demand for resources   
• Cloud data management services can drive major operational improvements. One asset manager stated that their quant investment team was able to reduce the daily computational time required to drive their quant strategies from 19 hours to 1 hour per day
• NIIT Tech is providing support for moving to a poly-cloud environment (i.e. multiple third-party cloud vendors). NIIT Tech’s Infrastructure-as-a-code offering is effective for working in a poly-cloud environment.  

Summary

NIIT Tech is transforming itself to capture digital transformation opportunities and is investing heavily in IP and partnerships. It has built IP using open source code, which delivers solution modules that can be deployed with 40% less cost, time, and effort.

The key to NIIT Tech’s success has been its ability to deliver services and outcomes quickly and effectively. Accordingly, NIIT Tech’s employee training and digital tools all focus on delivering functionality and high speed. As one NIIT Tech executive put it, “Data is king, speed is emperor”.  

At the 2019 Infosys Confluence North American event last week, we spoke with execs about how Infosys has been driving accelerated growth in the BFSI sector in North America. Most of Infosys’ revenues in BFSI are in Banking and Financial (BFS) services, where the recent growth been based on the following major activities:

• Installations of Finacle: in the past two years, Finacle, which now has 540 installations globally, has seen increasing adoption in North America. Key to this has been a focus on modules that solve immediate client problems, and then radiating to additional modules as required. Key high demand modules in the North American marketplace for Finacle include:
  • Digital engagement suite
  • Payments platform (electronic payments)
  • Commercial banking
  • Setting up a new digital bank (Finacle has regulatory approval in 50 states, something only a few legacy ISVs have)
• Reengineering sales engagements: Infosys has restructured its GTM from an offering siloed approach to a client management team approach. This change in approach has helped deliver improved topline growth in North America, from 4.5% (as reported) in FY18 to 8.1% in FY19, outstripping both Accenture and TCS
• Design-led transformation engagements: e.g. creating the branch of the future for a regional bank.

Current areas of focus in BFS segments include:

• Mortgages: this is the largest single LOB revenue generator in the retail banking industry. In Europe, Infosys has recently formed a JV with ABN AMRO’s mortgage operations in the Netherlands, taking a majority stake (see our note here). And in the U.S., Infosys has acquired the entire IT team of a regional Charlotte, NC lender. It has also recently hired an industry veteran to rationalize its North American mortgage go-to-market activities and grow the business. The ability to scale operations up and down in the notoriously volatile lending industry is a key reason some lenders are adopting Infosys’ cloud-delivered mortgage services. Using its regional centers, it expects to grow its local lender business to 20% of its overall U.S. mortgage revenues  
• Wealth and Asset Management: W&A managers need to launch new fee-based products to market rapidly to grow their business. The Infosys services team has been able to support new product development based on its work with market infrastructure providers. The Finacle platform allows new modules to be set up quickly (often in a quarter of the time an internal set-up would take). The reduced time to market provides significant sales acceleration to the client. Revenues from this type of engagement, and the pipeline, have grown rapidly over the past two years.

Banking clients we spoke to said that they anticipate continuing to move to the cloud (the reduction in time-to-market making the cloud value proposition compelling even when the cost is higher) and remain committed to moving to a hybrid cloud environment. They anticipate that the next big technology disruption in their sector will be the adoption of ML, which will accelerate over the next 24 months and start to deliver robust value.   

Infosys’ banking sector business has regrouped its GTM, with a clear focus on providing agility to banks so they can reduce their time-to-market with new product introductions. Infosys claims typical cost reductions of 40-50% and reduced time to market. Their increased sales growth indicates it is working well.

At the same time, Infosys has continued to invest with technology and operations acquisitions and will continue to increase its footprint in Western Europe and the U.S. One challenge will be handling volume swings when the market turns.    

A major focus at Infosys’ client event was its Live Enterprise (LE) approach - we will shortly be publishing a separate blog on this.

The BFSI industries are increasingly looking to intelligent automation (IA) to address key challenges in their business. A recent NelsonHall survey of 50 BFSI executives found that they perceive that IA is able to replace manual processing and human decision-making, allowing the bank’s operations to decouple processing volumes from headcount. Key benefits respondents expect to receive from IA include:

• Improved handling of low value transactions (100% of respondents)
• Reduced cost (98%)
• Improved service fulfillment times (96%)
• Reduced error rates (92%)
• Improved customer experience (88%).

However, achieving these benefits has frequently been challenging because:

• Use cases are domain intense, allowing at most 50% reuse for similar processes 
• Implementation is labor intense, often requiring 4 to 6 months of time and costing up to $1m 
• Unmanaged bots can learn the wrong things and “go off the rails” over time, destroying value
• IA is not one technology, but multiple technologies working in concert, which makes effective systems coordination critical to delivering a useable solution.

The complexity of these challenges requires IA vendors to create a roadmap and deliver an offering which targets a narrow range of problems. Typically, there needs to be three types of participant in the development of an IA offering:

• Systems integrator: who will identify client needs, implement the customized solution, and manage the ongoing operation of the IA solution to assure continued effectiveness
• Academic partner: who will provide advanced cognitive technological tools and insight to develop a differentiated offering
• Product vendors: to deliver COTS RPA and basic IA tools to the offering.

Let’s look at how one vendor developed an IA offering, and then at an example of how it was deployed.

HCL’s EXACTO

HCL decided to create an IA offering in 2016, focusing their product on the processing of unstructured data. They began their process by partnering with a leading U.S.-based university, which had developed analytic solutions for processing radiology images. The image processing capability would be useful for processing physical paper documents used in banking contracts, procurement, and handwritten documents.

HCL launched its IA product, EXACTO, in 2017. The underlying platform is built on open source machine learning libraries. The product uses servers with GPUs to run Deep Neural network algorithms. EXACTO can integrate into an existing workflow application with a single API. It has four differentiating capabilities:

• Image processing: fixes distortions, removes noise, and sharpens images
• Text recognition: extracts text from heterogeneous fonts/handwriting. Detects multi-languages
• Domain ontology: provides data correction and search-and-sort fields from data streams
• Deep learning and natural language processing: extracts localized characters and classifies documents.

HCL claims EXACTO achieves 85% to 95% accuracy on typewritten documents and 60% to 65% accuracy on handwriting. It processes documents in three stages:

• Digitization of document images
• Classification of the output into categories
• Extraction of data from the digital output, which is then fed into the target application.

Let’s look at how EXACTO was applied to a very manual, paper-based processing environment.

Trade processing at a global bank

Trade processing is well known for using faxes across very large numbers of parties to conduct business, and frustrates attempts at process automation. The process has a T+1 reconciliation window, which drives increased costs as labor needs to be applied in volume to meet the deadline. Initial errors from manual processing are enhanced by errors introduced by manual processes in the audit trail. Typical errors include:

• Trades erroneously marked as processed in the source system, when they have not been entered into the target system
• Trades marked as duplicate when two similar, but different trades are received from two different brokers
• Data entry discrepancies at the time of trade booking which are not caught until reconciliation.

EXACTO was applied to the processing of faxes and paper documents. It classifies documents using a domain ontology and extracts text into a digital form which is then processed.

Benefits from the use of this IA solution include:

• Reduction in average handle time of 60%
• Enhanced accuracy due to machine learning
• Improved audit trails and compliance
• Can identify duplicates and correctly tag them
• Continuous automated evaluation and improvement of process
• Can run multiple instances to process large volumes
• Able to process 24/7, unlike humans
• Auto-capture of data without the need to configure each input template.

Summary

As described above, IA is enabling institutions to read documents and pictures faster and more accurately than humans. It is then able to process the resulting information with greater accuracy and speed. The traditional cascade of errors is mitigated, improving regulatory compliance and customer satisfaction. The key to success in this endeavor is finding the appropriate technical IP (in this case in a partnership between domain experts at HCL and image reading technology at a leading U.S.-based university) to solve complex interpretation challenges. The resulting solution can process low-value transactions at high volume and with high accuracy. This type of solution will increase the number of high-volume, low-value transactions which banks will be able to deliver profitably, expanding the range of possible products they can introduce to the marketplace.   

EXACTO is trademarked by HCL.

Blockchain has been a focus of technologists, VCs, and media pundits for several years now. However, to date, operational deployment remains minimal. Of all enterprises already working with blockchain, only a few percent have a scale operational deployment. Blockchain has seen three stages in its short history:

• Lab testing of the technology: 2014 to 2016. Key events include the launch of Ethereum, Hyperledger, and R3
• POCs: 2016 to 2018. Key events include the launch of BaaS (IBM, Microsoft JV), B3i (insurance), Marco Polo (trade finance), MOBI (mobile DLT), IIN (interbank information network)
• Operational usage: 2019 onward. Key launches include NASDAQ LINQ, JP Morgan JPM Coin, Binance and the recently announced Facebook’s Libra.

Activities are slowly moving from exploring the technology and what it can do to finding out where it can be profitably applied to business cases.

Achieving effective operational deployment

Businesses are finding that key steps in making effective operational deployments include:

• Identifying compelling use/business cases, where key components include:
  • Business value: cost reduction needs to be significant and ongoing
  • Transaction execution speed: distributed ledger technology currently does not execute quickly. However, the evolution of private consensus mechanisms and permissioned blockchain networks, has addressed many of the concerns for DLT over speed and scalability
  • Ecosystem openness: closed systems (e.g. exchanges or closed payment platforms) are inherently more likely to see an alignment of partner goals and values
• Building the right reference architecture: an enterprise blockchain platform is more than just a blockchain technology stack. It encompasses Infrastructure (data, storage, network), security (including auditing), applications (templates, IDE, testing, integration), and operations (monitoring, smart contract management, ecosystem management) in addition to the platform itself (consensus management, smart contract execution, etc.)
• Identifying relevant partners, including product and business partners. The partners need to have as compelling a need to join as the promoter’s need, which often has not been the case
• Creating an effective governance mechanism: mechanisms require addressing the needs of regulators, participants, and customers
• Adoption: buy-in requires both a thoughtful explanation of cost/benefits and a forceful statement of ecosystem requirements for continued participation as a member. The priority for promoters of a blockchain proposal is to maintain trust while participants are undergoing the major operational changes blockchain brings.

Example of successful blockchain deployment application in Banking

So, where can blockchain be deployed effectively in an operational environment? I caught up with Capgemini recently on one of their blockchain engagements that has gained significant traction. In this case, Capgemini deployed blockchain to address KYC in the banking industry. The client is a consortium of banks that wanted to reduce the cost of its combined interbank KYC activities. Key aspects of the engagement were:

• Challenge: Banks typically conduct KYC/AML verification with manual, multi-step processes. Processes are repeated across banks and internally across departments for the same customer. The consortium wanted to simplify and automate processing to reduce cost and error rates. By sharing KYC/AML data for a given customer across departments and banks, the consortium wanted to reduce duplication of static data processing

• Scope of engagement: Capgemini designed a hybrid Hyperledger Fabric/R3 Corda solution that enables KYC data collected by one institution to be validated and shared among multiple institutions. Each participant bank can fill in the complete KYC profile with elements of their own data. The resulting KYC profile is more robust since it can be verified by more than one participating institution. Every bank shares the same decentralized master copy of KYC data
• Benefits:
  • Reducing cost from a reduction in duplicative processing and increased automation
  • Improving speed at which KYC record documentation is completed due to the reuse of static data, rather than reconstruction of data for each KYC report
  • Increasing accuracy resulting from data sharing.

The characteristics of successful deployments

The case above highlights that successful deployments of blockchain are often characterized by:

• Input processes taking data from multiple, heterogeneous sources, which are then manually processed with little standardization across internal or external silos
• Output processes delivering relatively simple, highly standardized reports (e.g. go/no go decision on doing business with this entity)
• Participants sharing the goal of obtaining the same output with no competitive pressure and proper governance mechanisms
• Blockchain technology creates a statement of record which should be used to eliminate repetitive reconstruction of static data for each transaction. This creates efficiency by eliminating steps.   

Institutions looking to deploy blockchain trial 100 POCs on average to find ~3 use cases to operationalize. By looking at the characteristics of successful deployments, institutions will be able to focus their experimentation on projects with a much higher likelihood of being valuable in a business setting.

NelsonHall recently attended the TCS Innovation Forum 2019 in New York. The forum demonstrated clear progress in TCS’ thinking and approach to Business 4.0 since last year’s conference – TCS has identified human engagement and buy-in with the principles of Business 4.0 as critical to successful implementation and value realization in business transformation. And human buy-in is applicable at multiple levels in an enterprise’s journey, not just the initial buy decision.

Successful Business 4.0 projects are different from traditional projects at three key levels:

• Approach to technology: the approach changes from large project scope with minimal component transparency (using long development roadmaps, employing siloed teams using waterfall development methods) to small project scope with high, ongoing stakeholder feedback (employing scrum teams using agile development methodologies)
• Use of solutions: effective use of solutions requires the employment of meta-frameworks to articulate business metrics and technical criteria for solution selection and deployment. Large libraries of experience-sourced benchmarks underpin the weighting of each criterion for specific environments considered  
• Sourcing solutions: the emphasis shifts from standardization on a solution suite, to selecting the optimum solution for a specific task. Because much of the functionality is only available from emerging product vendors it is necessary for integrators to use larger ecosystems of product partners, which are continually evolving. 

Multiple client conversations presented at the conference highlighted how digitalization of the business has changed the approach required to solve operational challenges. Key examples given across sessions included:

• Data management KPIs are changing as processing data is less of a challenge, while curating data is becoming more of a challenge: deep learning can use vast quantities of data. Today, the algorithms to analyze data are robust, but data quality remains a key challenge. Several clients discussed their efforts to reduce the amount of data analyzed and draw conclusions from a more limited, but much higher quality data set
• Testing simulations to reduce resource and time requirements while increasing learning feedback:  systems do not work in silos, but planners and controllers operate in task silos. Globally optimized system development requires analytics support to allow developers to understand complex system-wide interactions. Increasingly advanced enterprises are using digital twins to allow technologists to shorten their learning curve in real-time to produce effective project deliverables
• Data democratization: Harvesting greater value from data requires more stakeholders to access and process that data. Advanced enterprises are increasing appropriate access to the use of data while masking components which need to remain private. Increasingly, enterprises are developing sophisticated strategies for determining what, where, when, and how access is granted 
• Reverse innovation: Innovation on large legacy systems is proving to be less effective than de novo projects where there are no legacy systems, procedures, or fiefdoms to defend. Enterprises are launching new ideas in emerging markets and then they (or TCS) bring those projects back to mature markets when fully vetted.

TCS and several clients provided a deeper dive into their data activities in a breakout session. TCS’ data services strategy is underpinned by three offerings:

• DATOM: a data and analytics maturity assessment, consulting, and advisory framework that enables customers to drive their growth and transformation strategies at the board level or CxO level, leading to multiple downstream initiatives
• DAEZMO: a framework that includes Machine First accelerators and leverages cloud, containerization, DevOps, data virtualization, etc. to modernize the existing data landscape to be business 4.0-ready  
• Decision Fabric: a cognitive business engine that enables the automation of complex business processes and powers contextual industry offerings.

The underlying solution accelerators support the move to cloud-delivered, agile data management services. TCS sees the following shifts occurring in data management activities:

• Data gathering and curating: currently consuming 50% of enterprise applied effort, this will shift to just 10%
• Data analysis and decision: currently consuming 10% of enterprise applied effort, this will shift to 50%.

Repeatedly, clients stated that they did not find the move to cloud saved them money. However, it did make real-time and near real-time analytic computations possible, which created significant differentiation in time-dependent processes. Often these processes were customer-facing, which increased sales closure and/or increased CSAT. Effective cost control required a shift in management focus from the cost of provisioning services (old model) to cost of controlling usage (new model).

The challenge in innovating operational systems has become a challenge of changing people’s mindset to focus on the key levers that new technology offers. Therefore, emerging markets are often producing the fastest adoption of digital technology, as there is no legacy mindset to overcome. TCS works with the largest institutions to support this type of change. It is just now creating the productized offerings that will be able to support mid-market firms adopting digital technologies (business 4.0) en masse.

NelsonHall recently attended the EY Global Analyst Summit in Boston. EY has grown its revenues at 8.5% CAAGR over the past five years, while investing heavily in technology and adapting its business model to become an IP-based services vendor rather than a provider of pure labor-based services. 

Here I take a quick look at EY’s wavespace offering and how it is being used to provide value for large enterprises. EY believes that large enterprises have many good transformation ideas but fail at execution. To improve execution of transformation projects, EY uses Geoffrey Moore’s “Zones to Win” taxonomy, which defines four project types each requiring a different mix of resources to succeed. EY believes it provides three core competencies to solve the disruption challenge:

• Design of services
• Business model innovation
• Engineering.

EY’s goal is to drive large ($100m plus) transformation projects. EY’s wavespace network, launched in March 2017 with 15 centers, has now developed to a network of:

• Flagship centers, with a full range of services in each center. There are currently 20 flagship centers
• Satellite centers, with a sub-set of services and focused expertise. There are currently ~2x the number of flagships centers 
• Pop-up centers, which are flexibly available. These are client/engagement-specific temporary centers focused on a specific challenge a client is facing, delivered from the cloud to an EY or client site.  

Wavespace events at a center require pre-planning to pull in the right mix of:

• EY subject matter experts
• Client stakeholders with the domain responsibility and knowledge
• Third-party partners with expertise and IP.  

Events run for one to three days, and EY wavespace delivers 700 events in the U.S. per year. As EY has developed its delivery model for wavespace, delivery has moved from a location-based event to an as-a-service offering independent of a physical place. This allows global organizations to develop their own customized offerings in a highly decentralized fashion.    

The consulting services EY delivers are supported by:

• Proprietary IP, focused on tools to massively source data/capabilities online and harness these resources to enable small teams to apply them to specific projects:
  • EmbrYonic: a cloud-based AI platform to analyze relationships between traditional and disruptive businesses. It tracks VC and M&A flows on 6.5m companies   
  • Transformation hub: a learning portal used by EY clients looking to implement technology products
  • Cognistreamer: a collaboration platform for external and internal stakeholders to collaborate. Enables enterprises to crowdsource solutions to problems
  • Storybook: a SaaS-based platform used by enterprises to understand how their customers move through their offerings.
• Digital Factory Layer: these are the capability modules within the wavespace centers:
  • Research lab
  • Design studio
  • Innovation hub
  • Showcase
  • COE
  • Delivery center.  

Examples of how BFS clients are engaging with wavespace include:

• Citibank Canvas: Canvas is a crowdsourced beta testing community established to improve customer experience. Since the inception of Canvas, Citi has experienced an 11% increase in brand favorability
• Global universal bank: uses the research lab to continuously monitor customer sentiment and CUX best practices
• Global retail bank: long-term use of wavespace to transform processes in trade finance, F&A, CUX, capital and profitability, Finlab, data management, and analytics. The bank is using wavespace to digitize its $17 Bn legacy operations platform investments. 

EY’s vision of digital transformation is focused on effectively bringing together large ecosystems of participants to solve enterprise challenges. EY has built its wavespace center offering to coordinate bringing the right participant at the right time into a workgroup. Clients who have engaged with wavespace have typically returned with ever larger engagement remits, as the growth in the centers and engagement activity demonstrates.

EY has been wise to maintain a narrow product focus (e.g. platforms such as SAP) and narrow client focus (large enterprises). Other vendors have set up sandbox centers like EY’s wavespace, but they retain sole or dominant presence in their centers. EY has taken a bold step, aggressively opening its centers to third-party participants. EY’s business model is moving from labor-based delivery services to orchestrator of services delivery. However, wavespace’s continued success will require maintaining managerial effectiveness over third parties who are outside of traditional control mechanisms.

I recently attended the WNS influencer conference in New Orleans, the theme of which was 'Co-create to Outperform’. WNS believes that the marketplace understands what transformation means for their businesses, but the challenge is how to achieve this vision. WNS’ view is that, for transformation to be effective, it needs to be customized for the client with their full participation in the co-creation process. The conference presented many examples of how a ‘two-in-a-box’ engagement was able to identify and implement an effective solution to a challenge which delivered both very high savings and high satisfaction.

Client feedback

WNS presented seven clients who outlined their engagements and why they chose WNS, with several key themes emerging.

All clients referenced:

• WNS enjoys very high client retention. Each client presenter had worked with WNS for multiple years and several were clients for over 10 years
• Culture fit is very important and a key to WNS’ success
• WNS is a vendor who works with clients across a wide range of sizes including middle market enterprises. In the banking industry WNS focuses its services on financial institutions with assets from $50 Bn to $200 Bn. Banking industry clients present stated they preferred WNS because they were important to WNS at their size.

Most clients referenced:

• A focus of WNS’ work with clients is SCM/F&A
• Delivery model included WNS as manager of multiple third parties (‘one throat to choke’)
• Many engagements required the move from ‘tribal knowledge’ to standard operation procedures, without antagonizing the client’s people or culture
• WNS was introduced to them by word of mouth from satisfied clients. For example, two clients  had not initially included WNS in their RFPs because they reached out to ‘the usual suspects’ for proposals. When the proposal process was not progressing satisfactorily, WNS was drawn in at a late date to bid due to a referral based on a successful engagement of the same scope and focus. During the bidding process, WNS turned out to be an exact fit for their needs.

Client example: regional bank

A northeast U.S. regional bank provided an example of WNS’ engagement style and benefits delivered. The bank began its relationship 12 years ago, when it was looking to improve its operational efficiency by moving some processes offshore. Over time, the relationship has grown due to:

• Cultural alignment: the bank places a high value on interpersonal relationships. It operates in close-knit communities where the standard of interaction is high support for the customers and communities. WNS is comfortable working without reference to a contract, once it has been signed, and without change orders, unlike other vendors. It has also enabled WNS and the bank to co-create solutions to improve processing efficiency   
• Flexible staffing model: WNS has been willing and effective at flexing staffing levels as volumes swing, including when there have been unanticipated volume swings
• Periodic process reengineering: WNS has identified process reengineering opportunities and fulfilled the technical work to implement those changes. The bank is a frequent acquirer of other banks, which provides a steady flow of these opportunities   
• Robotics: RPA is difficult to implement effectively. WNS has been effective at co-creating with the client to build and implement RPA solutions which meet and continue to deliver the business objectives the initial business case envisioned.

The example the bank presented was of an RPA implementation to capture information from legal documents. The bank had hired a consultant who identified the opportunity and built the business case. The consultant intended to implement the RPA solution, but the project failed to move forward. The bank called in WNS who took over the project and, working with the client, co-created the final automation solution, including implementation. The department previously processed these documents manually, with an annual budget of $750k. Currently, the department is processing these documents for $329k per year, a cost reduction of 56%. When the system has fully matured, the anticipated cost reduction will be 67% of the annual budget. The bank summarized the relationship benefits as: their investments into the relationship, the quality of work delivered, and the cultural fit.

The WNS approach

WNS’ approach to digital transformation for the banking space targets an underserved market, medium-sized financial institutions, to transform manual-intensive processes into automated processes. WNS works with these clients to create solutions customized to support the client’s differentiated value proposition in the market. Few vendors are willing or able to apply resources to middle market or regional engagements that create customized outputs. This allows WNS to apply its domain knowledge, which is embodied in its employees based on their industry experience, to solve operations challenges to drive outcomes relevant to industry and market-specific requirements.

WNS has been wise to maintain a narrow process focus in each industry. Execution is critical to a project’s success, especially so with RPA engagements (where, post-deployment, most RPA deployments increasingly lose effectiveness due to poor bot oversight). In addition to clients in the audience, there were prospects who are considering RPA engagements with WNS because of poorly performing RPA engagements with existing vendors.

I recently attended the official opening of Infosys’ design center in Providence, Rhode Island. In 2017, Infosys committed to hire 10k workers in the U.S. by 2022. Part of that commitment is a plan to open six training and delivery centers across the U.S. intended to provide benefits including:

• Partnering with local colleges who have specific capabilities such as design education, which are critical for delivering digital services to enterprises, but in short supply with existing workforces
• Delivery centers for these skillsets which can work closely with regional and national enterprises to address legacy processes which have been a challenge to address using offshore delivery
• Creating a more intimate relationship with Infosys clients from onshore.

The six centers Infosys committed to build are:

• Indianapolis, Indiana: target 2k workers by 2022
• Raleigh, North Carolina: target 2k workers by 2021
• Hartford, CT: target 1k workers by 2022
• Phoenix, AZ: target 1k workers by 2022
• Richardson, TX: target 500 workers by 2022
• Providence, RI:  target 500 workers by 2022.

A closer look at the Providence center

The Providence center was officially opened on February 12, 2019 but has been operating since summer 2018. Its initial client is a major bank. The center was initially established with a partnership between Infosys and Rhode Island School of Design (RISD), a leading school for industrial design located a few blocks from the center. RISD was ranked number one in 2015 and 2016 for graphic design, printmaking, and industrial design by QS World University Rankings. The partners are contributing:

• RISD: coordination of course development with Infosys, classroom instruction, and student placement services into the center and Infosys workforce
• Infosys: identification of relevant work skills required, internships, and jobs for graduates.

To date, the center has hired 100 employees (ahead of plan). Half the hires are from RISD and half are from Community College of Rhode Island (CCRI).

At the opening, Infosys and CCRI announced a partnership which committed each organization to work to train and employ CCRI students in relevant design technology skills. The impetus for the partnership is that community colleges teach 50% of the college level students in the U.S., but these graduates obtain relatively few job offers relative to their numbers. By providing relevant work experience, Infosys and CCRI expect to increase the rate of job search success for these graduates. To date, Infosys has found that the CCRI students have a higher level of job performance and morale than their typical employees. The partnership uses the DEAL (Digital Economy Aspirations Lab) as its development venue, where students learn, in a corporate environment, skills that are immediately relevant to Infosys and other employers. In addition, DEAL will sponsor two joint task forces:

• Identifying entry-level roles suitable for community college students across industries, and creating paths to move into those jobs
• Articulating the value of these experiences to four-year colleges so that students can receive credits from those colleges to apply towards four-year degrees.

Conclusion

Skills to deliver digital technologies are in short supply globally. A key value of digital technologies is the ability to engage people much more effectively. Infosys has taken this challenge and built a differentiated center focused on industrial design technology implementation for its existing clients (mostly tier one global enterprises). It has built the center next door to one of the top colleges in the world for industrial design. By working with local colleges Infosys is able to ensure that skills are learned which are immediately relevant to the work required by Infosys’ client engagements. Industrial design is fundamentally a creative process, which means each worker is a unique asset. The colleges identify students who have the capability to succeed in creative work; Infosys then works with the school students to develop relevant skills. The result is a differentiated design capability created in Infosys’ workforce.

The next decade will see a rapid growth in demand for industrial design capabilities across industries, as 5G, open banking, and omni-channel delivery infrastructure becomes operational. As data and channels grow, customer engagement will become the critical differentiator for successful enterprises. Creating a workforce with advanced design capabilities at scale is necessary to capitalizing on this new environment. This center is a first step in the industry to shifting the ITS proposition to differentiated delivery.   

The banking industry cycle has turned towards austerity for 2019 as indicated by recent events, including:

• Labor cost cutting: State Street announced 1.5k executive layoffs, and Santander will close 20% of its bank branches in the U.K. Both are part of long-term trends incurred by automation and the shift to omni-channel delivery  
• M&A activity: Cost pressure is driving banks to sell their operations centers to third-party services vendors. For example, in the past month, Cognizant acquired the operations of three Nordic Banks which were joined into one vehicle, Samlink. Also, long-time banking operations industry consolidator Fiserv acquired First Data, a payment processing services and solutions vendor, to increase its scale in payments
• Tightening financial conditions and slowing of economic growth: U.S. Fed and other central banks raising interest rates, and slowing economic growth in the U.S., China, EU, and U.K. 

These events underline conditions where the financial services industry will face slower revenue growth and will need to aggressively reduce costs to remain profitable. Because of this, we expect 2019 to be a strong year for banking outsourcing, with banking ITS and BPS markets growing as fast in 2019 as in 2018. Below I identify five key growth areas for 2019.

IT outsourcing

Banks are merging or selling unwanted branches and lines of business, and this trend will continue until the next recession. M&A will drive IT outsourcing deals, as banks look for temporary labor to integrate targets quickly to realize efficiency benefits.

Core banking platform expertise will be key to winning deals, but digital technologies will be required to support the agility needed to cost effectively reengineer the operations of both the acquiring and acquired banks. Currently, Europe is showing the highest level of activity in this area. Later in the year we expect the U.S. to accelerate. Asia will remain a laggard in this area over the next year.     

Open banking

In 2019, open banking will take off, due to regulatory deadlines requiring go-lives in 2019, as banks look to monetize their assets. Open banking is the concept whereby banks open their platforms to third-parties for them to transact business with the banks’ customers and suppliers. And currently, banks are playing with business models, pricing schemes, and target customers.

The banking industry provides no direct comparable offerings to guide banks looking to monetize open banking assets. Setting up a business will require significant investment in security and vendor quality controls before the first dollar is made. Expect there to be many missteps along the way. ITS vendors working on infrastructure enablement will be the ones to make money in 2019. We expect interesting ideas to come to the fore from 2020 onwards. It will take five years for successful business models to take root and consistent earnings to start rolling in.

Automation & AI

RPA and AI implementations have been growing rapidly for the past three years and will continue to do so in 2019. Key initiatives for 2019 will be for services vendors to improve their use case development and create the ability to manage RPA bot groups.

RPA use cases do not make their cost projections over 60% of the time when deployed in POCs. Development of use case libraries and improved analysis is mitigating, but not eliminating, this challenge. Vendors are now repurposing successful use cases across clients and geographies. 2019 will need to be the year where vendors and banks consistently identify winning use cases prior to POC deployments. Vendors who succeed in this challenge will be able to deliver much higher return on engagements for their clients. 

Management of deployed bots has been a significant challenge for banks and vendors. By integrating AI into controller bots, vendors can increase the uptime and effectiveness of bot teams. Where bots operate 24/7, it is more effective to automate the management than to have humans managing with shift handoffs.

Cloud delivery

Banks are finally willing to aggressively make the transition to cloud delivery. Currently, the primary venue for cloud is on-premise. However, to achieve aggressive cost takeout under conditions of rapid IT infrastructure/application change, it requires external cloud delivery from a shared environment.

Banks are grappling with redefining the internal/external operations split. In 2019, banks will articulate what needs to remain internal (high value, non-repetitive processes) and what can be delivered externally (low value/less differentiated processes). Operations will still need to integrate these two types of processes effectively. Cloud delivers high cost savings, but on a small operational footprint. Enlarging the operational footprint is the highest cost saver and will be undertaken by successful banks this year.   

Transaction processing management

Banks will focus on transaction processing management. Regulations requiring real-time payments have led to new regional platforms which deliver instant payments, including NPP in Australia, RTP in the U.S., and Instant Credit Transfer scheme. Now banks need to understand and manage these high-speed transactions within their internal operations. Banks will deploy AI to achieve better, more efficient processing of transactions. The AI will need to pull and process data in real-time to stop unwanted transactions and report suspicious ones. And because the transactions are between counterparties, banks will seek third-party IT services vendors to support processing and coordinate across counterparties.

The primary purpose of Wipro'ss Digital & Big Bets Analyst Relations day in Boston at the end of November was to outline its digital and automation strategy, including presenting some startup partners Wipro is working with (and has invested in through Wipro Ventures) to help drive forward this strategy. Wipro believes its key technology bets will enable it to help clients reinvent their businesses as digital businesses.

Here I take a look at Wipro’s four ‘big bet’ initiatives, with examples from the financial services industry, and provide feedback from two of Wipro’s startup partners.

The four big bets

Wipro has selected four key areas as its big bets:

Digital

Its digital big bet is around the application of technologies and methods to human interaction, especially customer contact. Wipro presented a case study of a mortgage lender whose legacy mortgage origination process was long, complex, and difficult to navigate for originators and borrowers. The result was a very low conversion rate for borrowers who started an application.

Key components of the solution were mapping processes, bringing in stakeholders to discuss challenges and preferences, designing an improved customer journey, and implementing the platform. The result was improved NPS and conversion rates.

Cloud

Cloud delivery is a foundational bet which underpins all of Wipro’s automation initiatives. Wipro presented a case study of a major financial data provider which used an inflexible legacy platform for its data services business. The platform was inflexible, costly, and operating on aging infrastructure.

Wipro was able to re-platform the legacy apps to AWS while doubling memory and servers. AWS delivery enabled improved BCDR. The result was a 64% reduction in OPEX and a one-year payback.  

Cybersecurity

Here, Wipro emphasized the importance of making it simpler and easier for clients to manage their cyber risks. Currently, Wipro has ten platforms and is increasing the number of partners it works with to address this ongoing challenge. With reference to the Equifax breach, Wipro stressed that the most important feature of cybersecurity is not to perfectly secure the environment, an unattainable goal, but business continuity after a breach (with continuity requiring minimization of operational losses and trust with stakeholders). It stressed that setting client expectations, using the latest techniques, and providing customers with high levels of transparency are the key elements.

Industrial and engineering services

Here, Wipro is helping clients design products, improve them, and bring them to market. This is typically applicable to manufacturing clients, not financial clients.

The viewpoint from Wipro’s startup partners

I interviewed representatives from two startups Wipro has invested in, both of which are security vendors with large financial services client bases. They spoke to the value of the Wipro relationship and how they deliver value to clients:

Demisto

Demisto is a security orchestration, automation, and response (SOAR) provider. 25% of their clients are F500 companies, including tier one banks and payment processors. Their platform has three key elements:

• Case management and tracking tool
• Automation to accelerate response time
• Real-time interactive investigations using machine learning based on analysis of actions, not data.

Demisto said that it benefits from the Wipro partnership due to:

• SI and IT staff who are familiar with their technology and can integrate it into client environments
• Increased sales opportunities in multiple geographies and industries where they have not previously been working. Currently, the partnership accounts for a significant percentage of overall revenues at Demisto, up from zero two years ago.

Tricentis

Tricentis is a vendor of automated software testing services for DevOps. They target tier one companies, including large financial institutions. Testing is provided for UI and APIs on the web and mobile systems. Tricentis uses a partnership model for testing delivery, and partner organizations generate 50% of its revenues. Wipro has partnered with Tricentis for 14 months and now has several thousand testers trained in the Tricentis toolset. Over the past year, Tricentis’ revenues have doubled due to its partnering strategy. Over the next few years, Tricentis will focus on testing SAP applications in preparation for the 2025 SAP move to HANA.    

Summary

Wipro is using the cloud to help tier one clients with ponderous legacy systems to become more agile and reinvent their business models. The challenge of a cloud environment is increased risk of cyberattack. Avoiding a cloud environment partially mitigates cyber risk, but not enough to overcome the cost and agility disadvantages of a legacy environment. Investments in cybersecurity are made to better manage the cyber risk that comes with cloud delivery and, more importantly, do it in a transparent way that maintains and improves customer confidence in Wipro’s services.   

On top of the cloud and cybersecurity platform, Wipro is investing in digital services to support client revenue growth. Financial institutions cannot focus solely on cost-cutting; they need to drive revenues to build a sustainable business model. Promoting positive customer engagement using digital services is driving client revenue growth. Enabling these capabilities requires emerging technology products which Wipro delivers via product investments and partnerships. The partners I met have experienced rapid revenue and customer growth because Wipro’s clients are demanding automation services, and Wipro is recommending and staffing the delivery of those solutions.

None of Wipro’s big bets are final solutions, but rather approaches with no final form. The final goal is flexibility to adapt to an everchanging environment at a significantly lower cost than previously possible (50-80% lower cost versus the previous 20-35%). While most digital competitors are pursuing the same goals, the sheer scale of partnerships, client engagements, and internally allocated resources demonstrates Wipro’s commitment, and the number of engagements executed to date validates Wipro’s initiatives so far.

I recently attended the Infosys Confluence event in California to look at the vendor’s activities in banking & financial services (BFS). Here are the key takeaways.

Under its current CEO, Infosys has taken a new software-agnostic approach to digital business, which is yielding results. Infosys has proprietary platforms, including Finacle, for core banking and NIA for AI. However, Infosys’ primary clients in financial services, tier one global banks, have legacy platforms they are unwilling to replace and digital solutions they have decided to standardize on for future implementations.

For Infosys, the software-agnostic approach means they structure their services to enable plugging in any solution the client prefers, enabling it within an IT ecosystem that will meet the business objectives of the client. To achieve that goal, Infosys has developed Digital Navigation Framework, which it announced at the conference. The five components of the framework are:

• Design +: digital design services to improve customer experience. Infosys has acquired several design agencies including Wongdoody and Brilliant Basics to enable these skills and is expanding its capabilities with a design center in Providence, RI in partnership with Rhode Island School of Design and studios across the globe: Seattle, LA, London, Amsterdam, Berlin, Dubai          
• Proximity +: localization strategy to enable closer work with client teams onshore and increase training to scale the workforce in scarce digital skills. Infosys has invested in two innovation hubs (Indiana and Raleigh), education & training centers and digital studios to not only cater to client requirements but also train the workforce
• Agile +: Infosys has invested $100m in agile development technologies, including the creation of its DevOps platform. Infosys enables clients to undertake agile software deployment projects using the DevOps platform, open source software, reusable digital templates, and its large library of APIs
• Automation +: Infosys is helping clients navigate their AI and RPA journey through its in-house platforms – NIA and AssistEdge, in addition to partnerships and competencies across the best in class industry platforms/solutions  
• Learning +: training to create and maintain the digital services skills required to deliver large-scale digital projects (which are in short supply in the marketplace). Infosys is advancing its localization initiative, announcing a new technology and innovation center in Arizona which should house 1k employees by 2023. Offerings delivered from these centers include:
  • Training of college students in partnership with schools to develop relevant skills leading to digital services jobs at Infosys or elsewhere
  • Retraining of Infosys employees with relevant skills for new types of Infosys engagements.   

Infosys has applied these principles in North America with its BFS clients who, over the past year, have reduced spending on compliance and redirected funds to digital enablement initiatives. Over the next twelve months, the banks intend to take monies from tax savings under the new tax law to drive forward more digital initiatives.

Infosys emphasizes that success in digital enablement requires prior experience working with industry legacy platforms. In the mortgage industry, 97% of loan servicing platforms are from a single vendor, which limits the level of value creation possible. But in origination, due in part to the wide variety of platforms in use, large efficiency gains are possible. In one recent engagement, Infosys enabled a legacy platform to automate most of the data management and the client was able to reduce headcount by 58%.

During the past year in North America, Infosys has added two new retail banking engagements (one a new logo and the other an existing client), both large contracts for mortgage lending services. The contracts rely on Infosys’ state mortgage licenses (currently 44 states) which allow their employees to support the client in regulated processes. Ramp-up has been rapid with over 55 FTEs added in the last twelve months.

There are, of course, challenges: I talked with one client who has been working with Infosys on several RPA POCs, where there was a challenge in integrating bots into the legacy platform to deliver targeted benefits. Successful RPA solution selection requires the client/vendor to balance tradeoffs between:

• Cost: industry standard RPA solutions (e.g. Blue Prism, Automation Anywhere, UIPath) cost significantly more than services vendors’ RPA solutions (e.g. AssistEdge) 
• Functionality: industry standard RPA solutions have more robust functionality and product development roadmaps than RPA solutions from services vendors
• Legacy platform: integrating bots into a legacy platform is challenging if the underlying software is incompatible. Therefore, banks with successful RPA programs have decided to standardize on one RPA vendor.    

Once the solution has been selected, successful implementation requires:

• Setting up a test environment: identifying the apps in the core platform which will need to work with the RPA solution. Set up a test environment which matches the production environment the final solutions will operate in
• Specific to mortgage operations: as mortgage operations is a document-intensive environment, successful RPA solutions require high capacity data extraction tools with AI functionality to manage both physical and electronic documents.

All the Infosys clients I have spoken with remain confident in working through startup RPA programs, or are glad they stayed the course where they have successful RPA programs. 

I will be researching RPA and AI services in BFS for my next market assessment this fall, delving deeper into adoption challenges and the opportunities these technologies provide to banks. 

This is the latest in a recent series of blogs on open banking, which is likely to become the biggest driver of change in the banking industry since double-entry bookkeeping swept the industry in Genoa during the 1300s. I recently talked with Virtusa’s xLabs, a digital innovation hub within Virtusa, about their approach to open banking and what their roadmap is for the future.

Open Innovation Platform

Virtusa xLabs believes a successful open banking environment will require rapid, successful, iterative innovation by banks and FinTechs. However, the inhibitors to innovation include:

• Lack of feasibility of some ideas: low idea maturity or lack of compatibility with legacy systems   
• Ineffective matching of ideas to the bank’s main business problems and funds
• Ineffective partnerships with other third-party innovation providers
• Poor user/revenue value due to lack of validation systems or awareness of technologies.

Virtusa xLabs has built its Open Innovation Platform (OIP) composed of three elements to reduce the impact of the last three inhibitors (funding, partnerships, and value) and allow individual innovators to focus on addressing the first of them (feasibility). OIP is composed of vendors providing:

• Idea management tools that can help with idea management, but not idea execution (e.g. Brightidea, Spigit, Wazoku)
• API management tools: technology providers providing non-industry specific integration tools and services (e.g. Mulesoft, WSO2, and ProgrammableWeb)
• FinTech curators/matchmakers: news providers/aggregators (e.g. MEDICI, CBInsights, Plaid, and Matchi.biz).

The OIP provides access to these resources to allow banks to convert an idea into an MVP in a few weeks rather than months. The effectiveness of the OIP relies in part on scale. Currently, the OIP’s scale of offerings include:

• API bundles: 200+ internal and FinTech APIs. APIs are categorized as:
  • Mock APIs: used in tests to determine if a concept works
  • Smart bank APIs: light APIs with no deep logic to use available data for analysis
  • Core banking system: APIs which can be used to test technology’s impact on an entire process and all dependent processes within a core platform  
• FinTechs: ~10k vendors cataloged by capabilities
• LOBs: 106 tables by LOB in the sandbox
• Investment and Trade LOB: data generation currently underway in xLabs’ AWS environment
• MVP/CVPs: 25+ completed
• Test bed: 10m customers and 40m transactions.

Virtusa xLabs supports clients using its OIP with four services:

• Problem identification: what are the credible use cases specific digital technologies can be applied to solving, and what technologies (at what level of maturity) are available from what vendors?
• Rapid project starts: the ability to set up a cloud-based platform to commence work without sharing sensitive data before a final contract signing with FinTech vendors. This allows faster project starts and completions
• Shared practices: an internal community portal to share best practices and reduce redundant processes for non-differentiating processes. Currently, there is a KYC internal community
• FinTech marketplace aligned to countries: facilitates vendor selection based on relevant domain expertise. FinTechs are categorized as:
  • Disruptive: Tier 1 banks use these FinTech vendors to support disruptive business model change
  • Catchup: Tier 2 banks use these FinTech vendors to support them in catching up to tier 1 banks regarding functionality and operational efficiency
  • Basic enablement: Tier 3 banks wanting to establish a digital presence face access challenges based on their legacy infrastructure. FinTechs focused on these issues provide COTS FinTech enablement for legacy environments.

NelsonHall perspective

FinTech has been slow to achieve its promise due to several factors. Experimentation to date has been active but ineffective, and most POCs do not meet their business case. More effective synthesis of domain expertise with technology should improve project conversion to useful operational change. And improving a bank’s ability to evaluate and select technology vendors will improve the rate of successful project generation and reduce the cost of achieving success. In addition, sharing best practices for non-differentiating processes will release industry funds to pursue disruptive opportunities, which require long development cycles and large resource commitments.

Virtusa’s OIP is creating a scale community where participants can not only find each other, but the OIP helps participants effectively search for the ‘best fit’ partner. Over time, Virtusa xLabs will need to move the focus of the OIP from accessing a wide range of technology vendors to fewer vendors with a greater domain focus on a few key geographies and business cases.

As FinTech offerings become more mature, technology vision will become less important than business execution. Currently, Virtusa seems to be developing a focus on reg tech, payments, and deposits, which are the core of retail banking. These processes require heavy customization by country and must be executed at high volume and low cost, just the type of processes that need disruption if banks want to remain effective in a digital world.  

I recently attended the TCS Business 4.0 conference in Boston to understand TCS’ latest developments in the BFSI sector. Here are the key takeaways.

BFSI represented 32.5% of TCS’ Q2 2018 revenues and generated $7.7bn in revenue over the latest twelve months. TCS reports 20% of BFSI revenues are derived from its digital offerings, with BFSI clients adopting digital technologies and services at a pace which is expected to accelerate over the next few years. TCS surveys of its own BFSI client base indicate 44% of banks intend to implement open banking capabilities in the next three years and 75% of banks expect to operationalize some form of blockchain platform by 2020.

Digital demand shift from B2C to B2B use cases

Over the last 12 months, TCS has seen a shift in BFSI client demand for digital services from B2C use cases (including digitalization of the customer experience and expanding the range/functionality of omni-channel engagement) to B2B use cases, which include:

• Capital markets adoption of digital technologies to support wealth advisors and investment managers
• Exchanges facilitating experimentation with new security types at lower cost, or B2B data management across independent organizations and compliance
• Blockchain experiments, which are finding highest adoption among large-scale ecosystems including exchanges and tier one global banks, which have communities with very high numbers of participants and transactions.

TCS is delivering these services using its Machine First Delivery Management (MFDM) network. Increased automation through MFDM helps to increase delivery predictability by reducing error rates, but requires the rearchitecting of the workforce structure, skills, and methods to increase delivery collaboration and agility across TCS’ Global Network Delivery Management (GNDM).

Workforce training has been critical to TCS delivering an agile skilled workforce. It has trained 235k employees on agile work methodologies, 67k agile practitioners, 11k agile certified, and 380 Ninja coaches. This training has resulted in 1.5k agile engagements, 80+ transformational programs, and ~100 large-scale agile adopter clients. TCS claims 20% productivity and 100% process velocity increases where it deploys agile techniques.

A value stream approach to innovation

To support client process innovation, TCS has shifted its operations delivery approach to a value stream orientation. This engages with LOB and operations executives to simplify processes and undertake “risky” processing innovation in the context of business value streams, not operations delivery processes.

Businesses have always competed on price, quality, and speed. Today, digital processing increases the value of speed, making it the prime differentiating characteristic in financial services. This requires transforming operations by failing fast and adopting new IP and procedures as needed. In the past year, banks have changed their buying requirements in the following ways:

• From lowering operational risk to mitigating operational risk required to meet business goals
• From single-site location of operational resources for scale economies to multi-location, coordinated delivery to achieve scale economies
• From development methodology to enterprise transformation methodology
• From tactical adoption to operations model transformation
• From humans executing tasks with machine support to humans coordinating machine execution.

Case studies

TCS illustrated the benefits of agile transformation with a case study of a large European bank for whom it provided cloud DevOps, unified collaboration, and multi-shoring services across 65% of the bank’s platform landscape. It involved ~160 agile teams, and 1.5k associates. The key benefits included:

• Time-to-market reduction: 30%
• Cycle time reduction: 80%
• Faster deliver: 30%.

Another BFSI case study focused on business disruption. A major global asset manager (that did not have a personal advisory business) wanted to enter the wealth advisory business with a focus on the mass affluent marketplace. Key differentiators in wealth advisory are quality of advice and price. The challenge was to create a scale business while maintaining a focus on individual customers. The scope of engagement covered two key business activities with robots and advisors working together:

• Customer acquisition:
  • Robot: customer data acquisition, needs analysis, and portfolio construction
  • Advisor: disclosures, modifications, and strategy approval
• Customer sustenance:
  • Robot: periodic reviews, event-triggered changes, rebalancing, and cash-out activities
  • Advisor: cash-in events, life events, and strategy/portfolio changes.

The wealth advisory business has been successful, as evidenced by:

• Growing the mass affluent business to $112bn AUM by mid-2018
• Service fee price points of >= 0.3% of AUM, with satisfactory business profitability
• Hybrid human/robot advisory model working effectively.

Since the financial crisis, regulators have been tightening the KYC processes banks are required to undertake. Enhanced KYC requirements have been applied across many regulations, including MiFID, PSD2, and ultimate beneficial ownership requirements (U.S. CDD Rule). And, as compliance requirements have increased, banks have spent increasing amounts of time and resources on addressing operational delivery of KYC.

NelsonHall estimates the costs of KYC compliance has increased tenfold over the past decade, and by an average of 26% per year. Yet, despite efforts to standardize approaches and share overhead for KYC activities, financial institutions to date are pursuing a wide range to approaches to addressing KYC compliance. I spoke recently with TCS about its initiatives in delivering KYC services to financial institutions.

TCS’ KYC initiatives

TCS has a large KYC practice with 3K employees, which is part of a larger 6K employee customer onboarding practice. Its clients are tier one financial institutions primarily based in the U.S. APAC, Europe and the U.K. Key domains discussed include:   

• Data management. As banks expand their target customers and markets, TCS provides recommendations to clients on:
  • Automation in data acquisition: in collaboration with relevant data vendors, adoption of intelligent automation techniques and a set of decision tree rules to facilitate the shift towards dynamic KYC
  • Enrichment of data, including managing the effects of data update cycles for various vendors, data quality by source, and application of best practice AI to data, which can improve data quality and reduce data discrepancies (often found at above 5% level across data obtained from multiple sources)
  • Data distribution, including applying full or partial KYC updates to silos across the bank which enable reuse of KYC data across multiple regulatory compliance requirements.
• Pricing. Vendors I have spoken with, including TCS, are willing to move to transaction-based pricing, but clients continue to select FTE-based pricing due to their negative view of current cost of operations for bundled services. Tier one clients have indicated an interest in moving to alternative pricing models, but the corporate culture will need to change first  
• Ecosystem. Banks do not want to manage a large ecosystem of IT vendors. Technology is advancing, with many new vendors emerging to support improved KYC processing. The key to success of a vendor ecosystem, in TCS’ view, is the flexibility and service orchestration frameworks which can integrate multiple solutions to commit to the desired outcomes 
• Blockchain and regional utilities. Banks are not ready for the coordination required to develop these types of cooperative facilities; the core reason remains each bank’s customization of its processes. Internal coordination of such facilities, across a bank’s LOBs, is the likely first use case. TCS is currently working on POCs with several clients for internal blockchain and utility facilities. 

The broader KYC picture

Despite claims that the industry is automating and digitalizing KYC processing, industry experience clearly shows that STP or shared services remain a distant goal. Key achievable steps towards that goal include:

• Automated data management across silos: when an event in one bank silo triggers a KYC update, the process updates KYC information across all bank silos
• Intra-bank sharing of best practices and overheads is achievable, but inter-bank sharing will not currently work 
• Increasing the level of standardization of KYC frameworks and processes will prepare banks for standardized pricing methodologies and shared environments. Banks are not currently interested in transaction-based pricing or industry consortia for shared standards or services
• Vendors are promoting technology vendor ecosystems to bring solutions to banks. Successful ecosystems maintain open interfaces because banks want point solutions included which they have vetted previously and have chosen to standardize on
• KYC is moving from a calendar-based refresh cycle to an event-based refresh cycle. To successfully conduct frequent refreshes requires:
  • Automation of data pulls and analyses to mitigate the cost of frequent processing
  • Effective placement of data findings across silos (e.g. placing customer nationality data in product silos across the bank).

Vendors are moving ahead with automation and AI services to enhance KYC and reduce the cost, but the banks’ siloed structures remain an impediment to rapid change. 

I recently attended the Capgemini Financial Services Industry Conference in London, also meeting up with several banks to learn more about where they are spending their digital services money, what they are looking for from digital projects, and where the market is headed.

Bank demand for digital services has grown and matured over the past few years. In 2015 and 2016, IT services vendors expected strong growth in digital services engagements from European banks, only to have their hopes dashed. Since early 2017, demand from European banks has been strong. In fact, so far in 2018 European banks have demonstrated white-hot demand. Global banks have allocated budgets of $1bn to $2bn for digital transformation over the next two years, and are committing to spending an additional 4x or 5x that money over the ensuing five to eight years.

Rapidly scaling to meet digital demand

To meet BFSI digital goals, Capgemini believes it needs to deliver at scale globally with relevant market and domain skills. Capgemini’s BFSI group works with 70% of the top 100 global banks and BFSI accounts for ~27% of its revenues. It has~200K employees globally and 55k of those are focused on financial services, half of them based in delivery centers and around the world close to its clients’ operations. Capgemini has been rapidly growing staff to meet the demand for digital services, with ~70% of hires as lateral transfers, and it leverages an ecosystem of enterprise partners, fintechs, academia, and industry thought leaders to support this growth ambition. Of course, effective orchestration is the key enabler of a successful partner ecosystem.

Capgemini has built a reputation among its clients as a vendor who can fix troubled automation projects. Most of its engagements for enablement services, such as RPA, come to Capgemini from failed projects, estimated at one-third of all RPA engagements. RPA projects face challenges because although the POCs work well, the production environment does not match a POC’s conditions.  In a production environment, robots break as systems change and data flows shift. Capgemini has been able to rectify RPA projects because of its knowledge of the legacy environments the robots are operating in.  Capgemini sees RPA as a ‘band-aid’ to maintain systems until they can be transformed to digital platforms. It will maintain its RPA practice, but expand its digital platform replacement services capabilities.

BFSI platform transformation

Capgemini has focused its BFSI digital services business on platform transformation, which we estimate represents 85% of digital revenues. Client examples presented included:

• Cloud migration: a global bank with multiple markets requiring continental standardization and consolidation of data and analytics
• CX enhancement: a global retail bank needing to utilize best CX practices from other industries to improve financial services CSAT
• Data analytics: managing data and analytics across >150 applications and silos to develop improved insights for a client
• Designing a new client business model: an emerging Asian bank created a digital banking model to grow its business aggressively across the vast Asian markets.

Each of these engagements required a large-scale global rollout with local customization.

The broader picture

Tier one banks are changing their business models to address new industry cost structures and competitive challenges. And the change requires legacy platform renovation to deliver customer interaction capabilities at much lower cost. Hence, banks are:

• Shifting to omnichannel delivery: the number of physical branches will be reduced, and remaining branches will deliver complex services using AI-augmented humans. The scale of the distribution channel transformation will be massive
• Moving from ‘acting as a principal’ to ‘acting as a broker’: this will require coordinating large numbers of third-party specialist vendors to deliver a broad range of financial services to customers. Delivering this business model change requires legacy platforms to become open banking platforms. Opening legacy platforms, in turn, requires experience in both legacy and digital technologies and the scale to work on transformation across multiple geographies
• Improving CX with improved fulfillment: this requires creating STP using technologies which can draw data across legacy silos
• Increasing commitment to cyber security solutions to mitigate the increased risk exposures from moving workloads to omnichannel and cloud environments.     

Capgemini is maturing its digital BFSI business based on its engagements with several key clients, which are mostly tier one global banks. Previously, Capgemini had focused its engagements on European market requirements. Today, it has expanded its multi-decade, application-driven engagements to support global platform renovation at scale.

Opportunities for strong business growth come to ITS vendors only during periods of rapid technological change, and the digital banking revolution is such a period of change. Capgemini has recognized the opportunity, is committing the capital to building its delivery machine, and has the customer legacy platform experience to capitalize on the opportunity. It is pulling in a wide array of digital capabilities to serve a very focused set of clients, and its client case studies underline how each engagement is part of a very long-term development roadmap.  

Atos has made an unsolicited offer for security, SIM cards, and payment cards technology vendor Gemalto. The offer is sizeable at €4.3bn (plus Gemalto’s net debt of €938m at end of H1 2017) financed in cash. It runs until December 15.

Gemalto is another major acquisition for Atos: in its fiscal year ended June 30, 2017, Gemalto generated revenues of ~€3bn, with an EBITDA margin of 17.1%. If the acquisition goes through, Atos and Gemalto will have combined revenues of €15bn, and an operating margin of around 10%; Atos would thus achieve the double-digit margin it has been targeting for years. NelsonHall has published further details of the proposed transaction in its Daily Tracking Service.

Perfect timing by Atos

Gemalto has issued several profit warnings this year related to its Payment and SIM card businesses, which last year represented 51% of its total revenues.

SIM cards are on a secular decline with the stagnation of mobile devices, and delayed investment by telecom service providers in newer SIM technology. The decline in SIM cards revenues accelerated in H1 2017 with a ~16% decline, where Gemalto was only expecting a 5% decline. Atos has stated it will launch a strategic review of the business, if it completes the acquisition.

Growth in its Payment business (smart cards used for payment cards, and related software and services, and mobile payments) in the past had been driven by the late migration of U.S. banks to the EMV standard. After a flat year in 2016 due to market saturation in the U.S., Payment revenues are down, by an estimated 16% in CC in 2017 YTD. Atos highlights that market conditions remain favorable in the mid-term with large geographies such as India still transitioning from a cash economy to electronic payments.

Gemalto has been investing in developing its cybersecurity portfolio, acquiring SafeNet in 2015 and 3M Cogent in 2017, and its M2M business (connected devices, tokens, and related software and security) saw double digit growth in Q2 and Q3 after a flat Q1. Some of its security solutions are experiencing difficulties (notably the Authentication business is transitioning from hardware to software), while others such Data Encryption are doing well. Other units are in different dynamics: Government security and identity is doing well while the Enterprise unit is challenged.

Following several profit warnings, Gemalto’s share value has been at its lower levels since 2011, in the €30-€32 range in the past month. Investors are moving away from Gemalto, not even encouraged by the reaffirmation of the full-year 2017 guidance. There is certainly an opportunistic element in Atos’ unsolicited offer: a 42% premium at €46 a share. This is not as generous as it may seem, as Gemalto’s share was trading at this level only a few months ago, in September.

Breton has also got the backing of state-owned investment bank bpiFrance, which owns a 8.3% stake in Gemalto.

And of course there will be TLCF benefits for Atos in France.

What does this mean for the Atos portfolio?

Currently, Atos has three main hardware and software businesses:

• Its Big Data and Security (BDS) business includes a variety of assets, including specialized servers, supercomputers/HPC, security software, command and control systems. BDS is doing very well and is expected to grow by at least 12% per annum. 2017 revenues will approach €700m
• Its Unify communication hardware and software business that it is currently restructuring and transitioning to cloud software. Unify overall is a €1bn business and is now trending towards revenue stabilization
• Worldline in the payment software and services business, with revenues of ~€1.5bn, and aiming for organic revenue growth to reach 6%-8% by 2019.

Then comes the philosophical question: does it make sense for Atos to further expand into software and hardware? If it acquires Gemalto, Atos would add another €3bn in revenues from a variety of software and hardware assets that would represent overall ~40% of the group’s revenues. Is Atos is spreading itself too thinly across over an extensive portfolio of IT services, hardware (from quantum computing to specialized phones) and software? 

And what are the implications for Worldline? Gemalto has software payment security capabilities that Worldline would be able to use for its payment services - and Worldline has been very open about its ambitions to be the European payment services consolidator.

Outside of payments, how complementary is Gemalto's security portfolio to Atos? And will Atos be able to find a potential buyer for Gemalto’s SIM card business?

Atos' profile is increasingly moving away from building a consistent IT services and payment portfolio to becoming a hardware-plus-services & solutions firm. While this may appear to be contrary to what has been the prevailing trend, Atos has demonstrable expertise in identifying promising targets at a competitive price, subsequently integrating and restructuring problematic businesses at pace, then driving profitable growth from the acquired capabilities.  When Atos acquired Bull, for example, Bull was a €1.1bn firm spread thinly across servers, HPC, software, and IT services; the technology part of Bull now lies in BDS, which has become a high growth and highly profitable business. However, Unify, in particular the S&P business, which Atos had been treating as a discontinued operation, is still a work-in-progress.

Gemalto would bring a new and different set of challenges for Atos and its approach to addressing these is not yet clear. However, on balance, Gemalto brings in a set of capabilities that could prove very useful to different parts of the Atos Group.

NelsonHall has just published a comprehensive Key Vendor Assessment on Atos which looks at both Atos (excluding its hardware businesses) and Worldline. For details, please contact Guy Saunders. 

In this article, I look at Mphasis’ Blockchain initiatives and at the segments they are focusing on for further development with their financial services clients. Mphasis began its Blockchain initiatives in 2016, initiating internal experiments and POCs to understand the technology and how it can be applied to business challenges.

Mphasis is working with a global financial services company on POCs and an approach to bringing a customer identity solution to the financial services market, in order to address consumer data challenges in a global environment. The customer and Mphasis are working to address multiple issues including:

• Solution construct, design approach, and related technology considerations to select the right Blockchain technology from different options such as BigchainDB, HyperLedger, Ethereum, Multichain, network transaction currency and conversion to fiat, engagement layer and access point technologies
• Industry ecosystem participation considerations – incentives, privacy protections, regulatory compliance considerations, trust and risk, and access point technologies to join the network
• POC prototype and demo – for an initial MVP.

The POC took 7 weeks to demonstrate that the technology works and compliance is achievable. The solution was set up as a multi-node environment that enables the industry participants to transact, by enabling functions such as set-up and administration, search, crypto-payments, transaction administration, analytics, regulatory oversight and access.

Since then, Mphasis has built an ecosystem of Blockchain tools and best practices, and conducted multiple POCs. Clients are narrowing the range of use cases they wish to pursue further and are driving some of those into production.

Mphasis’ Blockchain services & use cases

Mphasis has a core group of 10+ engineers working on Blockchain initiatives who are based in Bangalore. Key attributes of Mphasis’ Blockchain ecosystem include:

• POCs completed to date: 12, of which 50% were client requested and 50% internally undertaken
• Clients engaging on Blockchain: 7 across banking, insurance, and airlines
• COE founded: 2016
• Platforms employed: Ethereum, Hyperledger, Multichain, and Bigchain.

Mphasis focuses on the Etherium and Hyperledger platforms in its Blockchain work, and expects to add a capability in Quorum soon. Key POCs to date include:

• Trade finance for banks: enabling a decentralized network between importer, exporter, port authorities, and banks. Key issues addressed include document verification, fraudulent activity incidence, and document losses
• Mortgage document management: the goal is to store documents on the DLT as a customer goes through the loan application process. This will allow vendors (e.g. insurance companies) to access the documents and speed up TAT, which will reduce cost of origination and improve customer experience
• Record keeping: enabling a single version of the truth, with additional components including IOT and smart contracts
• Patient health records: enabling confidential sharing of patient records and with intended participants
• Baggage-as-a-service: distributed, decentralized system for tracking bags during travel by passenger using mobile device
• Group insurance claims: stakeholders including hospitals, insureds, insurer, and third-parties transact and exchange documents to enable fast settlement of claims
• Contract management: digital signing of documents on a Blockchain network to ensure transparency
• KYC registry: enabling a KYC market utility using Blockchain.

Going forward, Mphasis will focus on:

• Consulting for clients considering Blockchain initiatives
• Delivering Blockchain implementations (POC or operational) with integrated application suites to reduce time to market and increase platform efficiency
• Delivering operational support for Blockchain environments based on its solution experience.
• Continuing to create use cases around KYC registry, mortgage document management, trade finance, baggage-as-a-service, and group insurance claims.

Conclusions

To date, most Blockchain services vendors have been focused on enabling small groups of direct stakeholders to use Blockchain to eliminate the need for third-party support. Mphasis has focused instead on enabling stakeholders to bring in third-parties as customers, and use Blockchain as a highly secure, reliable self-service tool. This should allow data holders, the sponsors of these initiatives, to monetize their investments in customer data and documents. This will allow Mphasis eventually to transition its Blockchain services towards operations support and cybersecurity. By supporting its clients’ efforts to drive revenue growth, Mphasis is able to support compelling use cases for employing this technology.

In this blog, I look at Capgemini’s Blockchain initiatives and what segments they are focusing on for further development with their financial services clients.

Initially, Blockchain engagements were focused on: 

• Using POCs to develop an understanding of the capabilities and limitations of distributed ledger technology (DLT)
• Developing business use cases, trying POCs to determine if there is an effective business application of the technology
• Conducting due diligence on vendors to understand the supplier ecosystem.  

Recently, financial institutions have been narrowing the range of use cases and vendors they are willing to consider. They are looking to drive forward one or more use cases to full production, and their focus with Blockchain services vendors is to develop a selective roadmap for operational deployment of a few high priority engagements.

Capgemini’s Blockchain services & use cases

Capgemini has been pursuing Blockchain for two and a half years, and it has a group of 25+ engineers working on Blockchain initiatives, with seven engagements currently in play. Capgemini’s Blockchain practice believes successful initiatives require a combination of business domain and technology expertise, and it focuses on five areas:

• Technology expertise: especially DLT, cybersecurity, communications, and data management
• Domain expertise:
  • Structured finance: trade finance and factoring, non-listed, non-codified bilateral agreements
  • Payments: real-time international payments transactions, including compensation, settlement, and reporting
  • Capital markets: Post Trade Automation (including optimized Collateral operations), Syndicated & Commercial Lending, and Non-Listed Securities
  • Insurance and reinsurance: focused on European companies for smart contract management 
  • Digital identity: security and personal identity for access to the DLT
• Program management: DLT projects are complex and agile, with the client and vendor are working together on the project  
• Alliance partners: cloud providers, and product vendors. Capgemini participates on industry panels, especially on Hyperledger Fabric, to create and support roadmap development
• Partner on business: platform-based operations delivery. Creation and governance of the utility that will provide service to the clients.

Currently, Capgemini works with four key technology stacks:

• Symbiont
• Hyperledger
• R3 Corda
• Ripple.

Capgemini believes it is differentiating to understand the current state environment within a given client (both business processes and technology processes). Further, that understanding is required to be able to effectively reimagine processes using any advanced technology, especially Blockchain.  

Ultimately, Capgemini wants to act as a universal integrator, partnering with technology providers to support clients redesigning their business with Blockchain centric services that also leverage complementary capabilities like AI or machine learning. Capgemini is aiming to serve as the Transformation Partner for their clients, where Distributed Ledger Technology is the transaction framework to deploy next generation, collaborative operating models. Working with key partners, they will continue to evolve core technical competencies in Blockchain to its clients, such as:

• Blockchain as-a-service
• Security as-a-service
• Identity management as-a-service.  

Conclusions

To date, most Blockchain services vendors have been:

• Delivering POC engagements to clients as clients work to identify opportunities to use Blockchain technologies, or…
• Building Blockchain POCs for utilities they might productize for clients.

Capgemini is pursuing a third path of building on its extensive work with client legacy systems, and coupling that domain knowledge of the client with its own ability to coordinate multiple technology vendors to create faster, more effective business restructuring around Blockchain capabilities.

Ultimately, as Blockchain technology matures, Capgemini will transition to providing Blockchain infrastructure services focused on security and technology platform outsourcing. While the technology is still at a very early stage, adoption is increasingly looking to be done primarily by tier-one institutions. The technology will mature rapidly, and infrastructure providers will be harvesting most of the revenues being created for vendors in Blockchain.  

NelsonHall recently attended Capgemini’s financial services analyst conference in Boston, where the company discussed its activities and roadmap for the industry, which is focused on digital services. Here I look at how digital services are now driving Capgemini’s financial services business, with client examples.

Capgemini’s shift to digital financial services

Capgemini formed its financial services unit in 2007 and has grown its financial services business 7-fold from 2007 to 2017, increasing its share of Capgemini’s overall revenue from ~7% to ~28%. In 2014, the financial services unit started its own business transformation to focus on digital services for clients. Today, digital services for global financial institutions represent ~50% of its financial industry business and is growing five times faster than its legacy business. Capgemini’s financial services unit has a client base that is geographically diversified, with ~90% of clients evenly split between the U.S. and Europe, and the rest predominantly in APAC.     

Per Capgemini, and consistent with our research, financial institutions are anticipating severe cost compression over the next five years. For example, some capital markets firms expect 20% cost compression. These firms need to aggressively take out cost and have announced cost takeout programs (e.g. BNY Mellon and State Street) which are now several years old and still ongoing. However, the cost compression will not come in a predictable, straight line fashion. The capital markets industry prices its services based on assets under management (AUM). When the market declines, revenues fall due to declining asset values and redemptions. Capgemini is adapting its pricing mechanisms for hosted and outsourced services to follow the AUM-based revenue streams of its clients. This exposes Capgemini to greater revenue volatility, but should create greater client stickiness by supporting client margins regardless of volumes.

Client examples

The most compelling aspect of the conference was the client presentations. Each of the clients represented has substantially changed its business model to expand its lines of business beyond traditional boundaries. Previously the cost of expanding into new lines of business, with new customer bases and new markets, was cost prohibitive. Now, using digital delivery to lower the cost of entry, financial institutions are creating many new lines of business. Below are two examples of the client activities presented at the conference:

• Large North American bank: this client wants to drive revenues by using APIs to drive ‘headless banking’ and introduce new channels for product distribution. The bank used to launch only fully tested products. Now it is experimenting with launches of beta level products which are then developed in the market. Initial experiments indicate that new products will often require experimentation with pricing models, often derived from non-financial industries, to make the products successful in the market
• Large Asian bank: A well-established bank HQ out of Singapore started its digital initiative few years back and has 30 APIs in use for digital transformation. The bank has implemented many digital bank projects, but some of the LOBs are still in the process of completing their digital transformation. By publishing its APIs, demonstrating successful digital-delivered product launches, and using third-party ITS labor to mitigate the lack of sufficient digital talent to meet demand in Asia, the bank is changing its culture and LOB leaders are pursuing digital product launches as the first choice for new products (due to lower risk and higher expectation of winning new customers). 

In addition, new regulations are driving traditional ITS business, as compliance implementation deadlines continue to drive system modernization. Capgemini has a large payment ITS practice where it is currently working on PSD 2 compliance for European clients. PSD 2 allows any bank customer to use third-party service providers instead of the bank, and requires banks to provide APIs so those third-party providers can access the bank’s platform.

In summary

Banking is changing from a closed-platform industry to an open-platform industry. Digital services are both driving and enabling this change. Capgemini’s client legacy banks are transforming their businesses to adapt to open their platforms to allow customers to customize functionality and products they want to consume. Competitors and partners are gaining access to the bank’s platform to deliver services to the bank’s customers. Creating, managing, and curating APIs is the first step in this evolution. The next step is developing cognitive capabilities to manage the process well.

Finally, the banking industry is being forced to adapt business practices and models from other industries, such as pricing models, to successfully launch new products into the market. Rapid experimentation, coupled with the ability to identify and retain best practices, will be key to banks successfully managing their transition into digital institutions.   

Most Blockchain use cases have focused on reducing the need for (and cost of) infrastructure. And in Virtusa’s case, the vendor has focused on engagements where it can combine Blockchain technology with other emerging technologies such as QR codes, IoT, and encryption algorithms to deliver enhanced security and cost savings for environments lacking adequate supporting infrastructure. Here I take a look at Virtusa’s Blockchain initiatives.

Virtusa’s Blockchain services & use cases

Virtusa has been pursuing Blockchain for 3 years, and it has a group of 20+ engineers working on Blockchain initiatives, with 35 additional engineers in training in Hyderabad, who will be fully deployed by Q4 2017. Virtusa provides consulting and pilot services including:

• Strategy and design:
  • workshops for awareness and adoption
  • Use case creation and validation
  • Advisory on technology and vendors
  • Research on 400+ Blockchain startups  
• Sandbox:
  • Cloud-hosted experimentation
  • ~7 Blockchain variants, including: R3 Corda, Etherium, Multichain, Chain.com, Hyperledger, Quorum, and VP Blockchain
  • APIs to key platforms (primarily CRM and ERP)
  • Testing capabilities with very large datasets
• Accelerators:
  • 100+ pre-compiled use cases across multiple industries
  • Solution accelerators (listed financial industry only): payments, credit monitoring, check fraud, trade finance, OTC derivatives, interest rate swaps, and covenant management
• Advanced: 
  • Security (keyless cryptography, and homomorphic & format-preserving encryption)
  • Industry steering council participation in ISO TC-307 Blockchain and distributed ledger technology

To date, Virtusa has worked on ~100 use cases with clients, of which ~50 have been moved into pilots and remain active engagements. Of the active use cases, ~40 are in the financial services industry. Currently, Virtusa is working on three key use cases to develop them into operational deployments. The top three business patterns that establish strong use cases are:

• Provenance: check books or other financial instruments can be validated as authentic from a chain of ownership. Example: use of QR code on checks for retail bank customers to reduce check fraud
• Chain of custody: KYC, AML checks on transactions moving through an ecosystem. Example: rather than conduct comprehensive KYC/AML checks, as updates are required, banks can conduct KYC/AML checks from the last verified point in the Blockchain  
• Permission-based sharing of information: third parties can now share information securely based on homomorphic encryption (low cost) and format preserving encryption (used extensively today in the cards processing business) and benefit from the blockchain enforcement of rules to remove the need for a trusted third party. Example: use of IoT to log usage of farm equipment leased to multiple parties. 

Virtusa is moving all three of these use cases into production with its clients over the next ten months. It believes that its most differentiated offering is the permission-based sharing of information, due to its access to very low-cost, strong encryption technology. All three of these engagements are based in APAC/Middle East markets. Deployment of operational Blockchain environments in the mature markets of the U.S. and Europe are less likely in the short run due to strong existing infrastructure and the need to establish industry standards. However, changes in the mature markets, such as Brexit in Europe, and the recent announcement of support in production e.g.  Hyperledger fabric version 1 are likely to drive adoption because those changes will either require costly new infrastructure or a group of partners sharing a Blockchain environment.   

Conclusions

The case for Blockchain operations is developing fastest where institutions operate with little infrastructure (physical or institutional) and services vendors can combine multiple technologies beyond Blockchain itself, to deliver the functionality of a mature marketplace without the industry-wide investment required to create a mature marketplace. This favors business cases where banks operate in an emerging market or where a new bank product is getting deployed which does not have competitors in the market today.

By developing a set of use cases for Blockchain in banking, Virtusa can support clients who differentiate themselves by unique product offerings. Virtusa can help those clients reduce their time to market, which will provide the longest time in market with a product which has no close competitive offerings. By adapting the mix of technology products it combines with Blockchain technologies, Virtusa will also benefit from time in market with few or no close competitive service offerings.   

Many Blockchain services vendors have observed that up to 75% of proofs of concept for Blockchain fail to meet their goals. Analysis of drivers for such widespread failure indicates that the initial use case was flawed because it was constructed to justify experimentation rather than solve business challenges. However, TCS has focused its Blockchain efforts on developing uses cases that can drive successful adoption and, more importantly, define the ecosystem for successfully meeting a use case’s key performance criteria. In this latest blog on current Blockchain activities in the financial services industry, I look at TCS’ approach to Blockchain in banking.

TCS’ Blockchain initiatives

TCS has been pursuing Blockchain for 3 years, and it has a group of 100+ engineers working on Blockchain initiatives across all industries. In banking, TCS’ Blockchain group is based in Chennai. TCS’ primary goal is to develop effective Blockchain use cases for the banking industry, and to date has successfully developed 150+ uses cases across all industries.

The use cases for banks segment into key areas of interest for banks:

• Trade settlement (securities, FX, payments, etc.)
• KYC/AML
• Trade services (import/export). 

The largest demand for Blockchain services so far is for KYC/AML services. The key drivers for these areas of interest are processes where one of the following conditions apply:

• Process requires frequent document re-verification: KYC requires re-verification periodically, and for each new product sale. Trade finance requires re-verification as the documents pass along a chain of activities, with multiple counterparties
• Timelines and chain of activities must be attested: dispute resolution in trade settlement and trade services requires the ability to trace back to the point in time where a discrepancy in the interpretation of activity occurred.

The processes are primarily from closed loop transactions.

TCS offers consulting, ITS, and process audit services for Blockchain activities. In financial services, TCS has blockchain initiatives in retail banking, investment banking, capital markets, commercial lending. While TCS has not completed the implementation of blockchain project in operations delivery, it has done several POCs for customers in payments, securities settlement, trade finance, "know your customer" and supply chain finance. It is currently involved in a live Blockchain operations environment for a large global bank for Blockchain support of payments), providing audit support for the project. This allows TCS to enhance its understanding of what works and doesn’t work in a Blockchain environment, of which there are few, and none of scale, at present.   

TCS works with major Blockchain technology vendors including Ericsson-Guardtime, IBM, Microsoft, and associations (e.g., MIT Media Lab Digital Currency Initiative) as well as through its COIN partners. It has a proprietary Blockchain solution, which it deploys as required in its POCs, but does not sell as a standalone solution.

Conclusions

Global financial institutions are heavily experimenting with Blockchain to understand how and where to use it in their business – or even better, how to use it to change their business model. However, our research shows 70% to 80% of Blockchain POCs fail to meet their initial business case. The biggest challenge in Blockchain is understanding what makes a good business case, and getting stakeholders to cooperate on adoption. The technology, despite its arcane and novel characteristics, is not the primary impediment to adoption.

TCS is focusing its Blockchain efforts on developing a granular understanding of how Blockchain works, and when it succeeds in a business environment. This approach will create efficiency in Blockchain adoption for financial institutions because they will waste less effort on “a solution in search of a problem” and spend more resources applying the right solution to business challenges. TCS is not there yet, but headed in the right direction.    

IPSoft's Amelia

NelsonHall recently attended the IPSoft analyst event in New York, with a view to understanding the extent to which the company’s shift into customer service has succeeded. It immediately became clear that the company is accelerating its major shift in focus of recent years from autonomics to cognitive agents. While IPSoft began in autonomics in support of IT infrastructure management, and many Amelia implementations are still in support of IT service activities, IPSoft now clearly has its sights on the major prize in the customer service (and sales) world, positioning its Amelia cognitive agent as “The Most Human AI” with much greater range of emotional, contextual, and process “intelligence” than the perceived competition in the form of chatbots.

Key Role for AI is Human Augmentation Not Human Replacement

IPSoft was at pains to point out that AI was the future and that human augmentation was a major trend that would separate the winners from the losers in the corporate world. In demonstrating the point that AI was the future, Nick Bostrom from the Future of Humanity Institute at Oxford University discussed the result of a survey of ~300 AI experts to identify the point at which high-level machine intelligence, (the point at which unaided machines can accomplish any task better and more cheaply than human workers) would be achieved. This survey concluded that there was a 50% probability that this will be achieved within 50-years and a 25% probability that it will happen within 20-25 years.

On a more conciliatory basis, Dr. Michael Chui suggested that AI was essential to maintaining living standards and that the key role for AI for the foreseeable future was human augmentation rather than human replacement.

According to McKinsey Global Institute (MGI), “about half the activities people are paid almost $15tn in wages to do in the global economy have the potential to be automated by adapting currently demonstrated technology. While less than 5% of all occupations can be automated entirely, about 60% of all occupations have at least 30% of constituent activities that could be automated. More occupations will change than can be automated away.”

McKinsey argues that automation is essential to maintain GDP growth and standards of living, estimating that of the 3.5% per annum GDP growth achieved on average over the past 50 years, half was derived from productivity growth and half from growth in employment. Assuming that growth in employment will largely cease as populations age over the next 50 years, then an increase/approximate doubling in automation-driven productivity growth will be required to maintain the historical levels of GDP growth.

Providing Empathetic Conversations Rather than Transactions

The guiding principles behind Amelia are to provide conversations rather than transactions, to understand customer intent, and to deliver a to-the-point and empathetic response. Overall, IPSoft is looking to position Amelia as a cognitive agent at the intersection of systems of engagement, systems of record, and data platforms, incorporating:

• Conversational intelligence, encompassing intelligent understanding, empathetic response, & multi-channel handling. IPSoft has recently added additional machine learning and DEEP learning
• Advanced analytics, encompassing performance analytics, decision intelligence, and data visualization
• Smart workflow, encompassing dynamic process execution and integration hub, with UI integration (planned)
• Experience management, to ensure contextual awareness
• Supervised automated learning, encompassing automated training, observational learning, and industry solutions.

For example, it is possible to upload documents and SOPs in support of automated training and Amelia will advise on the best machine learning algorithms to be used. Using supervised learning, Amelia submits what it has learned to the SME for approval but only uses this new knowledge once approved by the SME to ensure high levels of compliance. Amelia also learns from escalations to agents and automated consolidation of these new learnings will be built into the next Amelia release.

IPSoft is continuing to develop an even greater range of algorithms by partnering with universities. These algorithms remain usable across all organizations with the introduction of customer data to these algorithms leading to the development of client-specific customer service models.

Easier to Teach Amelia Banking Processes than a New Language

An excellent example of the use of Amelia was discussed by a Nordic bank. The bank initially applied Amelia to its internal service desk, starting with a pilot in support of 600 employees in 2016 covering activities such as unlocking accounts and password guidance, before rolling out to 15,000 employees in Spring 2017. This was followed by the application of Amelia to customer service with a silent launch taking place in December 2016 and Amelia being rolled out in support of branch office information, booking meetings, banking terms, products and services, mobile bank IDs, and account opening. The bank had considered using offshore personnel but chose Amelia based on its potential ability to roll-out in a new country in a month and its 24x7 availability. Amelia is currently used by ~300 customers per day over chat.

The bank was open about its use of AI with its customers on its website, indicating that its new chat stream was based on the use of “digital employees with artificial intelligence”. The bank found that while customers, in general, seemed pleased to interact via chat, less expectedly, use of AI led to totally new customer behaviors, both good and bad, with some people who hated the idea of use of robots acting much more aggressively. On the other hand, Amelia was highly successful with individuals who were reluctant to phone the bank or visit a bank branch.

Key lessons learnt by the bank included:

• The high level of acceptance of Amelia by customer service personnel who regarded Amelia as taking away boring “Monday-morning” tasks allowing them to focus on more meaningful conversations with customers rather than threatening their livelihoods
• It was easier than expected to teach Amelia the banking processes, but harder than expected to convert to a new language such as Swedish, with the bank perceiving that each language is essentially a different way of thinking. Amelia was perceived to be optimized for English and converting Amelia to Swedish took three months, while training Amelia on the simple banking processes took a matter of days.

Amelia is now successfully handling ~90% of requests, though ~30% of these are intentionally routed to a live agent for example for deeper mortgage discussions.

Amelia Avatar Remains Key to IPSoft Branding

While the blonde, blue-eyed nature of the Amelia avatar is likely to be highly acceptable in Sweden, this stereotype could potentially be less acceptable elsewhere and the tradition within contact centers is to try to match the nature of the agent with that of the customer. While Amelia is clearly designed to be highly empathetic in terms of language, it may be more discordant in terms of appearance.

However, the appearance of the Amelia avatar remains key to IPSoft’s branding. While IPSoft is redesigning the Amelia avatar to capture greater hand and arm movements for greater empathy, and some adaptation of clothing and hairstyle are permitted to reflect brand value, IPSoft is not currently prepared to allow fundamental changes to gender or skin color, or to allow multiple avatars to be used to develop empathy with individual customers. This might need to change as IPSoft becomes more confident of its brand and the market for cognitive agents matures.

Partnering with Consultancies to Develop Horizontal & Vertical IP

At present, Amelia is largely vanilla in flavor and the bulk of implementations are being conducted by IPSoft itself. IPSoft estimates that Amelia has been used in 50 instances, covering ~60% of customer requests with ~90% accuracy and, overall, IPSoft estimates that it takes 6-months to assist an organization to build an Amelia competence in-house, 9-days to go-live, and 6-9 months to scale up from an initial implementation.

Accordingly, it is key to the future of IPSoft that Amelia can develop a wide range of semi-productized horizontal and vertical use cases and that partners can be trained and leveraged to handle the bulk of implementations.

At present, IPSoft estimates that its revenues are 70:30 services:product, with product revenues growing faster than services revenues. While IPSoft is currently carrying out the majority (~60%) of Amelia implementations itself, it is increasingly looking to partner with the major consultancies such as Accenture, Deloittes, PwC, and KPMG to build baseline Amelia products around horizontals and industry-specific processes, for example, working with Deloittes in HR. In addition, IPSoft has partnered with NTT in Japan, with NTT offering a Japanese-language, cloud-based virtual assistant, COTOHA.

IPSoft’s pricing mechanisms consist of:

• A fixed price per PoC development
• Production environments: charge for implementation followed by a price per transaction.

While Amelia is available in both cloud and onsite, IPSoft perceives that the major opportunities for its partners lie in highly integrated implementations behind the client firewall.

In conclusion, IPSoft is now making considerable investments in developing Amelia with the aim of becoming the leading cognitive agent for customer service and the high emphasis on “conversations and empathic responses” differentiates the software from more transactionally-focused cognitive software.

Nonetheless, it is early days for Amelia. The company is beginning to increase its emphasis on third-party partnerships which will be key to scaling adoption of the software. However, these are currently focused around the major consultancies. This is fine while cognitive agents are in the first throes of adoption but downstream IPSoft is likely to need the support of, and partnerships with the major contact center outsourcers who currently control around a third of customer service spend and who are influential in assisting organizations in their digital customer service transformations.

I recently attended the Avaloq client conference in Zurich. The conference was well attended, with ~400 attendees. Avaloq is on a roll, adding clients across offerings and markets for the past several years. Here I outline how they are doing it and what their next steps are.

Background

Avaloq is a privately held vendor of technology-based solutions and services to the financial services industry. It was founded to provide a comprehensive core banking platform, the Avaloq Banking Suite, to banks and wealth managers. Avaloq uses its own banking platform with all its clients, and its banking suite has strong wealth management functionality, which has been extended over the past few years to deliver mass market wealth management and retail banking functionality.

BPS delivery capability was added in 2011, when Avaloq expanded its services into BPS by acquiring a 51% stake in B-Source, a BPS provider founded in 1995. In 2016, Avaloq acquired 100% ownership in B-Source. In addition to its own modules and APIs, Avaloq also has partnerships with ~60 external software vendors to extend the scope of its platform’s capabilities.

Unlike most software vendors, Avaloq has made a strategic decision to move into operations delivery. Avaloq perceives the market is moving towards combined technology/operations delivery because of mounting cost pressures, which limit or eliminate the ability to provide internal IT staff or software delivery or maintenance. Unlike in previous decades, when client banks’ platforms reach the point of requiring a major overhaul, most cannot apply the resources to modernize the platform. Even for banks with those resources available, the business case does not justify an internally-led modernization.

Technology Strategy

Avaloq’s core platform is a proprietary wealth management banking platform which it is modernizing and adding retail banking functionality to. Key components of its development roadmap include:

• Modularization of its platform: it is rearchitecting its platform into modules which can be integrated into any other banking platform. Ultimately, Avaloq expects to have a set of modules which can be integrated into any client environment as the client chooses
• As-a-Service delivery: Both BPaaS and SaaS are the types of delivery targeted. Partnerships with a large group of vendors are a core part of the As-A-Service delivery model for Avaloq  
• E-Bank: digital delivery for traditional banks and startup banks requires a core banking solution which is designed to be flexible so that new functionality can be enabled with minimal implementation effort  
• Mobility: enabling mobile access to banks’ proprietary platforms and integration to Avaloq partners’ mobile solutions and services. Avaloq’s goal in enabling mobile access is to support banks’ attracting new customers 
• Advisory: via partner offerings, accessing multiple options for automated advisory services
• Usability and CSAT: using design thinking methodologies, Avaloq is developing improved user interfaces and portals which can be adapted to individual bank branding preferences while delivering greater intuitive ease of use for customers.

To deliver platform modernization for its client base, Avaloq has a staff of 450 developers in 3 development centers in Europe and Asia.

Avaloq has already deployed modules for clients via As-a-Service delivery for:

• Automated advisory  
• Customer self-service
• Wealth management.

The goal-based wealth management module will go live at year end 2017. Also in 2017, Avaloq will launch new software functionalities including:

• Ability to do online software upgrades, with either hot or cold rollovers
• Greater security features, primarily user identification features
• New compilers to provide faster innovation (not a client-facing feature, but one that impacts clients)
• Division of module components into smaller modules.

In addition, Avaloq is experimenting with technologies including:

• Blockchain (more on this in a subsequent blog post)
• Web and mobile usage tracking
• Enhanced AI using the client’s own data
• Machine learning
• Chat bots.

Go-to-Market Strategy

Avaloq has focused its go-to-market strategy on selling a combined technology and operations offering. This has been informed by Avaloq’s experience with running operations for banks in its BPO centers. These banks provide Avaloq with best practices and business cases across a wide variety of customer segments and requirements. The ARIZON JV builds on this model and will be running the back-office operations of the 270 banks of the Raiffeisen Group in Switzerland, (Raiffeisen is the 3^rd largest banking group in the Swiss market).  

Critical to meeting the client project requirements is understanding and refining the business case for transition. Avaloq focuses on understanding and developing the business case for a client’s proposed project. Third party vendors can source an array of services to meet platform modernization projects, a task that banks often find distracting from their ongoing business. The combination of understanding the clients’ requirements and delivery of modular functionality with operations execution has allowed Avaloq to sell to the wealth management divisions of tier one banks.    

Avaloq’s current markets include 25 countries in continental Europe and Asia. The target markets for future entry include the U.S. and APAC.

Avaloq will not enter a new market unless it has a scale entry opportunity. In practice this means it will only enter a new market when an existing global client decides it wants to enter a new market using Avaloq software. Thus, based on conversations with clients about their intentions, Avaloq anticipates entering the North American marketplace in the next 24 months and several more APAC marketplaces in the next 12 months. 

Conclusions

Avaloq is focused on developing and delivering a combined technology and operations offering. The most frequent buyers of their offerings are wealth managers facing a major systems upgrade. They have developed their domain expertize with their own executives who have worked for industry participants, including clients; and they have developed solutions which are used by local Swiss wealth management banks as well as tier one banks and international banks and wealth managers.

They have broadened this capacity with a development roadmap for their offerings that includes:

• Technology: based on work with clients and customers that provide design thinking insights, and then they have modularized their entire platform to allow clients to consume whatever functionality is needed when needed
• Ecosystem of vendors: currently ~60 vendors which include best in market for certain key functionalities and many emerging functionalities
• Operations delivery: SaaS, BPaaS, and cloud (public and private).

Avaloq is targeting a fast-growing niche of the financial services market, wealth management, with a combined technology and operations set of offerings. Its large partner ecosystem allows it to provide a wide range of enhancements which clients can implement in unique configurations to create highly differentiated wealth management businesses. This capability has attracted tier one banks to buy a wide range of services form Avaloq, unlike the typical tier one buying strategy of buying unitary services to implement into the bank’s overall operations program. Because of this, Avaloq is well positioned to build a business that services all tiers of wealth managers in multiple geographies. That would be a unique business in BPS for the financial industry. 

This is the second in a series of blogs on current activities, use cases, POCs, and pilots with Blockchain in the financial services industry. In this one, I look at some of what Wipro is doing to support banks and financial services companies in deploying Blockchain solutions.

Blockchain technology & services

Wipro has been active for the past three years in offering Blockchain consulting and development. During that time, it has worked primarily with Ethereum, and Hyperledger, to develop its Blockchain solutions. Wipro has decided to be agnostic about technology partners because of the rapid pace of development and innovations in Blockchain technology, but it does have partnerships for cloud-delivered services on Blockchain. Current partnerships for cloud-delivered Blockchain services include:

• IBM Bluemix
• Microsoft Azure DevTest Labs
• AWS.

In Blockchain, Wipro provides the following sets of services:

• Advisory: engagement with thought leaders and CXOs to ideate strategies, plan roadmaps, and build use cases
• Technology: building POCs, pilots and production solutions with clients
• Infrastructure:  BLaaS – Blockchain Lab-as- a-Service (which allows clients’ internal teams to experiment and co-develop with Blockchain technology).
• Blockchain network services : to build Blockchain networks

Use cases & POCs

Wipro has developed use cases and POCs across industries. In banking and financial services (excluding its insurance use cases), Wipro has focused its efforts on five critical use cases to date:

• Banking:
  • Skip trace
  • Cross-border payments
  • Trade Finance
• Capital markets:
  • Triparty collateral management
  • Delivery-versus-Payment (DVP)

Each of these use cases has active POCs deployed on Ethereum and Hyperledger. Blockchain POCs could potentially use additional technologies. For example, skip trace could be deployed in concert with Wipro HOLMES Artificial Intelligence Platform, to engage predictive analytics on where the skipped person may have gone to.

Business executives at clients are the primary buyers of Blockchain engagements. They are concerned with POCs which provide flexibility, quick deployment, and scalability. To facilitate achieving these goals, Wipro has been engaged in the following initiatives:

• Flexibility and Quick deployment: Wipro has been developing a set of use case frameworks to identify what works, including required technical tools, business cases, and product ecosystems. These frameworks of best practices codify learnings as well as challenges to rapid, effective deployment of Blockchain technology
• Scalability: Wipro has been a launch partner for the Enterprise Ethereum Foundation. In that capacity, Wipro has done extensive testing of scalability on various variants of Blockchain technology, including Ethereum and Hyperledger, which has provided it with the expertise to understand the possibilities and requirements for scaling a Blockchain solution for production grade enterprise level deployments.

Also, Wipro actively promotes and expands its Blockchain partnerships to broaden its capabilities in this rapidly developing ecosystem. 

Summary

The key to successful business use of Blockchain technology is the size of the network using the Blockchain. Network size is impacted by adoption, which is in turn impacted by cost incurred and potential value received. Successful technology services vendors must work on building that ecosystem with their clients for it to be successful. Technology services vendors will be able to have the biggest impact on cost reduction by reducing the ideation and buildout costs. However, insight into how technology interacts with business operations will provide precision into how value will be delivered. Value delivered is even more compelling for prospective network participants than cost issues in their decision process.

It will take several years for large-scale adoption of successful Blockchain ecosystems to be operational. The primary driver of successful adoption will be the development of large, effective ecosystems of participants. Technology services vendors have a large part to play in identifying a realistic roadmap and support the realization of that journey.