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Accelerating Use of AI: How TCS is Helping Financial Institutions


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.


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.  

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