Automating the Bank

The first step towards AI

Sudhir Jha

Senior Vice President, Head of Product Management and Strategy, Infosys

An increasing number of banks are beginning to consider using software tools to automate repetitive, rule-based processes consuming massive manual effort. One estimate says that the robotic automation market for Banking, Financial Services and Insurance companies will grow 75 percent every year to touch US$ 835 million by 20208. Many industry watchers believe this could be the year that Robotic Process Automation (RPA), also known as the digital workforce, comes into its own.

A 2016 study of RPA in the financial services industry revealed that three-fourths of organizations had tried their hand at it with either a proof-of-concept or a more intensive implementation9, seeking benefits ranging from cost efficiency and accuracy to scalability and freeing up of human resources that could be deployed into more value-enhancing roles.

But while RPA has elicited great interest, it still has miles to go to hit meaningful usage: in the above study, only 4 percent of respondent organizations reported widespread implementation, whereas another 13 percent said that they had started, but not finished, the process of adopting RPA throughout the enterprise.

What is the focus of these efforts? Are banks looking to automate any and all processes that are amenable to RPA, or are they operating within a narrow band? And is there a “right” way to adopt the technology?

Our experience of working with banks around the world says that the bulk of robotic software automation is targeted at front-end banking processes that are deterministic, either reactive or proactive, and well documented. Let us look at each descriptor briefly.

Automation capabilities

Front – Back End: Front-end processes are those where interaction between customer and bank happens over a User Interface such as a website/ web form, spreadsheet, or application where data is extracted through the UI itself. The back office processes, however, are yet to take advantages of modern technologies. The processes consisting of functions like clearing, settlement, payments, custody operations, reporting, and compliance can also benefit tremendously from AI technologies.

Deterministic – Non Deterministic: In a deterministic process, all the steps are known and hence easily automated. A request for resetting a password is an example of such a process. A non-deterministic process, on the other hand, is not as clear-cut because it may arise from one of several causes and accordingly, set in motion any one of multiple processes. As illustration, consider a website that is down. The problem might be with the application itself, or the network, or the user’s browser. Clearly, recovery can be automated only after identifying the reason for the outage using a probabilistic decision tree that analyzes the organization’s past history to arrive at the most probable cause. That is actually deeper Artificial Intelligence (AI), or more specifically, Machine Learning territory. This tells us that when banks seek to automate complex or uncertain processes, they might need to use RPA as a first step in deploying a solution involving some form of AI.

Automation imperative

Fully – Partially- Not Documented: A process may or may not be fully documented. It is obviously easier to document a deterministic process. But sometimes, even that is not possible for a variety of reasons – people with expertise leave the organization taking their knowledge with them; the organization lacks a holistic view of a certain process; the necessary systems and resources are unavailable, and so forth. Exceptions often go undocumented because while people have a good idea of what a functioning process looks like, they don’t have the same clarity about a broken one. Here, the bank would have to discover the process before it can document and then automate it. Once again, this might call for a more complete AI-based solution to examine all the exception logs from an application, or study the keystrokes at an agent’s desk to understand what is going on.

Which Processes to Automate: Reactive – Proactive – Predictive – Cognitive: Every banking process is triggered by something. Robotic Process Automation lends itself easily to processes that are triggered proactively or reactively. An example of a reactively triggered process is a relationship manager being notified as soon as his customer enters the branch. A proactive process is anticipatory; the automatic dispatching of a fresh checkbook when a customer issues the last check in his booklet, is an example of such a process.

A predictively triggered process is set in motion based on a reading of data patterns. For example, if a customer’s transaction activity drops for three months in a row, it might signal that she has moved some business to another bank, and therefore trigger a particularly attractive promotional offer to win her back. This process can be fully automated since it is rule-based.

When the trigger is cognitive in nature – requiring a human skill such as natural language understanding, for instance – it will take a combination of RPA and other elements of AI to automate the related process. Think of a simple service request sent via email. Robotic Process Automation can do nothing until an AI tool with Natural Language capability, such as a chatbot, reads the email and instructs the software.

The way forward

From the above discussion it is clear that banks can begin their journey of automation with RPA, but will someday need to step up to solutions that take advantage of other AI capabilities to automate their trickier processes (non-deterministic, not documented, cognitively triggered etc.). A bank’s progress from deterministic to predictive to cognitive processes marks the increasing maturity of its automation capabilities, as well as the increasing business value it generates from automation.

In the early stages of automating deterministic processes, the bank gains operational efficiencies. In the middle stages, when it uses analytics to drive operations and make predictions, the bank becomes more proactive and resistant to business disruption. When it manages to automate cognitive processes, the bank will get to a state where its systems learn on their own. At that point, knowledge management will play a vital role by reducing the bank’s dependence on individuals.

That being said, it is not necessary for a bank to transition sequentially from one stage to the other; it could choose to run multiple initiatives of varying automation maturity levels in parallel. However, before getting into anything the bank would do well to lay out its vision for the future and get the right partner on board. A vendor with full spectrum capability, and a strong track record in financial services automation would be an invaluable ally in this important journey.