Big Money Automation: RPA in Financial Services

Finance and technology have a strange relationship in the current industry which is changing rapidly

While much of the tech innovation is driven by financial institutions looking for ways to increase the bottom line, many such institutions still rely on outdated legacy systems and a lot of manual processing and checking. In this context, the development of business-ready RPA in the financial sector could have a large impact on profitability, as evidenced by the Bank of England chief Mark Carney’s prediction that 15% of finance roles could be phased out in the coming years by robotic processes.

So what is RPA, the practice at the heart of automation in finance? As Leslie Willcocks, researcher at LSE School of Management, said in an interview with McKinsey: “RPA takes the robot out of the human. The average knowledge worker employed on a back-office process has a lot of repetitive, routine tasks that are dreary and uninteresting.

RPA is a type of software that mimics the activity of a human being in carrying out a task within a process. It can do repetitive stuff more quickly, accurately, and tirelessly than humans, freeing them to do other tasks requiring human strengths such as emotional intelligence, reasoning, judgment, and interaction with the customer.”


The nuts and bolts of how RPA works is quite complex, but the “need to know” aspect is that bots can now track and mimic human behaviour to learn how to create automated processes for these tasks, and this can be carried out quickly and easily without the need for a programmer to write scripts and establish the rules themselves.

Applications of RPA in finance

Accounts receivable: This might be one of the clearest use cases for RPA in the finance function. Anyone with experience of SAP Finance software will recognise the laborious tasks involved in dunning and other debtor management business processes, which can require strictly defined but at times varying steps to be taken when looking for payment. While it has been difficult to program software to carry out these tasks before now, the advent of machine learning allows RPA solutions to track and build rules based on how clerical staff process these routine tasks.

Investment management: There is a sweet spot for robo-advisors in the investment advisory market. While investors with large amounts of capital will always benefit from the fees paid to investment managers, this personal advice is out of reach for most everyday investors, the ability of robo-advisors to give generic but useful advice to investors based on their portfolio profile could benefit a large and currently underserved market.

Managing technological migration and legacy systems, data management: RPA is not only useful for everyday operation, it can also aid in business change. As we mentioned above, many financial institutions are wrestling with outdated systems that require herculean efforts especially with regards to data migration. This can result in tech transformations involving as much manual input as the old systems they are in the process of replacing.

One insurance company in the Caribbean, Guardian Group, successfully implemented RPA for streamlining and speeding up its move to a new suite of tools. RPA was able to “access, calculate, copy, paste, or use embedded business rules to interpret, use, and enter data into the core enterprise application.” Based on how clerical staff navigate between systems, inputting and copying data as they go, RPA can learn the optimal routes between diverse arrays of systems.

Insurance policy creation: For insurers, a lot of new customers require tailor-made policies based on their circumstances. However, the majority of new policies could be considered “boilerplate”, not requiring much human expertise. For these a-few-sizes-fit-all policies, RPA can establish best practices and provide these policies to the right customers with minimal human oversight.

Verifying the claims management process for insurers: Further along the insurance lifecycle, claims management often requires repetitive checking by clerical staff, checking payments and documentation from various systems which is hard to build scripts for since it requires a heterogeneous set of systems consulted. As with other RPA applications, machine learning can easily establish work processes for this kind of activity.

Regulation compliance and reporting, KYC/AML checks: Since much of compliance requirements are obviously highly rule-based, this area is ripe for RPA applications. KYC/AML checks, for example can differ a lot in their implementation but are not heterogenous enough to preclude building an automated workflow using RPA tools.

These usecases should give a grasp of the basic functioning of RPA in finance, as well as the potential it holds. The broader context of this change is important in terms of measuring the impact and dynamics of RPA-led business processes. Other breakthroughs like the use of chatbots, process mining, and cognitive computing are playing their role in the adoption of RPA. In turn, this wave of new tools will result in a new workforce of professionals that work with these tools to realize increased productivity and efficiency.

The advent of AI tools like RPA will not just involve a one-for-one replacement of human action with machine action, the reality is that business processes (and business models in some cases) will in turn need to be reconsidered based on the new options available. More and more of the workforce will be employed in leveraging the capabilities of these tools, and less will work in the processing of repetitive tasks.

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