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HCL Targets Industry-Specific Processes with RPA - Significant Presence Developing in Banking Sector

HCL began its robotics program in late 2013. Since then HCL has invested ~$1.5m in robotics, (ToscanaBot Automation Framework), via its HCL ToscanaBot center of excellence, which currently employs a team of ~25 personnel and is planned to grow to 50+ personnel by 2016. HCL estimates that its robotics practice currently has an FTE impact of around 2,000 with this expected to grow to ~8,000 FTE impact by 2016.

HCL is offering robotics both in the form of robotics software plus operations to its new & existing BPO contracts. HCL is typically deploying robotics in two forms:

  • Virtualized workforce, directly replacing the agent with robotics (~70% of current activity by value). Here HCL estimates that 50%-70% efficiency gains are achievable

  • Assisted decisioning, empowering the agent by providing them with additional information through non-invasive techniques (~30% of current activity by value) and achieving estimated efficiency gains of 20%-30%.

In general HCL is aiming to co-locate its robots with client systems to avoid the wait times inherent in robots accessing client systems using a surface integration techniques a through virtual desktop infrastructure (VDI).

The principal sectors currently being targeted by HCL for RPA are retail banking, investment banking, insurance, and telecoms, with the company also planning to apply robotics to the utilities sector, supply chain management, and finance & accounting. Overall, origination support is a major theme in the application of RPA by HCL. In addition, the company has applied robotics to track-and-trace in support of the logistics sector.

HCL currently has ~10 RPA implementations & pilots underway. Examples of where RPA has been applied by HCL include:

Account Opening for a European Bank

Prior to the application of robotics, the agent, having checked that the application data was complete and that the application was eligible, was required to enter duplicate application data separately into the bank’s money laundering and account opening systems,.

The implementation of robotics still requires agents to handle AML and checklist verification manually but applying automated data entry by ToscanaBot robots and presentation layer integration thereafter removed the subsequent data entry by agents leading to a refocusing of the agents on QC-related activities and an overall reduction of 42% in agent headcount.

Change of Address for a European Bank

This bank’s “change of address” process involved a number of mandatory checks including field checks and signature verification. However, it then potentially involved the agent in accessing a range of systems covering multiple banking products such as savings accounts, credit card, mortgage, and loan. This led to a lengthy agent training cycle since the agent needed to be familiar with each system supporting each of the full range of products offered by the bank.

While as in the first example, the agent is still required to perform the initial verification checks on the customer, robotics is then used to poll the various systems and present the relevant information to the agent. Once the agent authorizes, the robot now updates the systems. This has led to a 54% reduction in agent headcount.

Financial Reporting for a Large U.S. Bank

HCL has carried out a pilot with a large U.S. bank to address the challenges inherent to the financial reporting process. Through this pilot HCL proposes to replace manual activities covering data acquisition, data validation, and preparation of the financial reporting templates. In this pilot HCL estimates that it has achieved 54% reduction in the human effort and double digit reduction in the error rates. FTEs are now largely responsible for making the manual adjustments (subject to auditor, client and fund specs) and reviewing the Robot output instead of the usual maker/checker activities.

Fund Accounting for U.S. Bank

HCL has also carried out a pilot to address fund accounting processes with a U.S. bank. The principle was again to concentrate the agent activity on review and exception handling and to use robots for data input where possible. Here once RPA was implemented, following the introduction of workflow to facilitate hand-offs between agents and robots, the following steps were handled by agents:

  • Upload investor transactions

  • Review cash reconciliation

  • Review monetary value reconciliation

  • Review net asset value package

Robotics now handled the following steps:

  • Book trade & non-trade

  • Prepare cash reconciliation

  • Price securities

  • Prepare monetary value reconciliation

  • Book accruals

  • Prepare net asset value package.

This shows the potential to automate 60% of those activities formerly handled by agents.

In addition, HCL has implemented assisted decisioning for a telecoms operator, with robots accessing information from three systems: call manager, knowledge management, & billing, and in support of order management for a telecoms operator. In the latter case, order management data entry required knowledge of a different system for each region, again making agent training a significant issue for the company.

HCL’s robotic automation software is branded ToscanaBot, and as an integral part of the Toscana Suite which also includes HCL’s BPM/workflow software.

ToscanaBot is based on partner robotic software. The current partners used are Blue Prism and jacada, with in addition Automation Anywhere currently being onboarded. In the future, HCL plans to additionally partner with IPsoft and Celaton as the market becomes more sophisticated and increasingly embraces artificial cognition within RPA.

HCL aims to differentiate its robotics capability by:

  • Combining robotics within a portfolio of transformational tools including for example ICR/OCR, BPM, text mining & analytics, and machine learning. In particular, HCL is looking to incorporate more intelligence into its robotics offerings, including enhancing its ability to convert non-digital documents to digital format and convert unstructured data to structured data

  • Process and domain knowledge, HCL has so far largely targeted specialist industry-specific processes requiring significant domain knowledge rather than horizontal services and is working on creating add-ons for specific core software applications/ERPs, to facilitate integration between ToscanaBot and these core domain-specific applications

  • Creation of IP on top of partner software products.

Within BPO contracts, HCL is aiming to offer outcome-based pricing in conjunction with robotics, but in some instances the company has just sold the tools to the client organization or provided robotics as part of a wider ADM service.

Overall, HCL may be lagging behind some of its competitors in the application of RPA to horizontal processes such as F&A, though HCL is applying RPA to its own in-house finance & accounting process, but is at the forefront in the application of RPA to industry-specific processes where the company has strong domain knowledge in areas such as banking and supply chain management.

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