Leveraging RPA to Reduce Risk and Increase Compliance in BFSI

Robotic Process Automation (RPA) is non-invasive, easy to implement, and delivers quality at a fraction of the cost. In this post, I've outlined how RPA can be leveraged to streamline risk and compliance for organizations in the Banking, Financial Services, and Insurance (BFSI) sector. To be truly successful, BFSI organizations need to first identify the areas that are most suitable for automation.

What are the top three challenges that keep managers in the financial sector awake at night? Take a minute to think. Got it yet? I’ll tell you:

• (Re)building client trust post-financial crisis

• Organizing risk management across different branches

• Optimizing and growing your business sustainably

Part of the disruption BFSI organizations are facing is the growing number of compliance regulations caused by a global landscape dominated by the risk and regulatory agenda.

The numbers paint a clear picture – banks often have to keep track of over 200 individual regulatory changes per day on a global scale, and this has more than tripled since 2011.

What’s more, a financial company spends approximately $60 million per year on Know Your Customer (KYC), due diligence, and client onboarding processes. By the end of 2016, financial organizations in the US alone paid fines of around $321 billion.

These are just two examples that show the impact risk assessment and compliance regulations are having.

The case for RPA

Finding an innovative solution to help navigate the compliance maze should be a priority for any organization. A clear characteristic of risk and compliance operations is the necessity of collecting information from multiple sources.

This is where RPA comes in.

As software able to mimic a human employee’s actions on a computer to execute end-to-end business processes ranging from ordinary to complex, RPA can run in the background individually or can be triggered by a co-worker for collaborative work. RPA excels in processes that require accessing and aggregating data from different sources. It provides accuracy, is non-invasive, easy to implement, and delivers quality at a fraction of the cost required otherwise.

Where to start?

Organizations must know where to start. It’s not enough to understand that RPA applies to those rules-based processes that are high-volume, manual, or repetitive. You must first identify the areas that are most suitable for automation, how complex they are, and what the potential business impact is. Organizations should look at processes related to KYC, regulatory monitoring and data collection, risk assessment, and account closure.

For KYC alone the figures are overwhelming. In 2017, financial institutions spent $150 million in 2017 on KYC procedures, with expenses expected to grow by 13% in the next 12 months. Similarly, onboarding new clients now takes 26 days, from 24 in 2016, and firms anticipate a 12% growth by the end of 2018.

General Data Protection Regulation (GDPR) is another hot topic where RPA can make a difference. With many organizations still struggling to fully implement the appropriate technical and organizational measures required by the European Union, organizations can leverage RPA to comply with GDPR. This includes automating citizens’ access to their personal data, automatically withdrawing their consent, informing the appropriate supervisory authority within 72 hours of a data breach, removing data that no longer serves a purpose, categorizing personal data, and even providing pseudonymization.

From rules-based to reasoning-driven automation

Not all compliance processes are deterministic. Some involve reasoning and therefore require RPA to borrow from other expert technologies such as natural language processing (NLP) and machine learning (ML).

As part of the KYC process, identifying a client’s main business countries from its annual report involves reading line-by-line documents that run to hundreds of pages, as well as pinpointing the paragraph and page number where the countries are found. By combining RPA with NLP and ML, robots can not only read and extract the data, but also classify it in order to reveal those countries where business operations are conducted.

There’s a lot more to be said about artificial intelligence’s (AI) infusion into RPA, a very exciting topic that we will cover in future blogs. Meanwhile, it’s never too early to implement RPA and see what it can do for you. UiPath is free to try and free to learn.

This post was originally published on the Capgemini blog. Andrew Rayner is VP of Customer Success at UiPath.