The banking sector seems earmarked for the implementation for Robotic Process Automation (RPA), with the cost cut from replacement of humans in mundane, repetitive tasks with Artificial Intelligence aggregating to the tune of $416 billion, across industries, by recent studies. Even in reports conducted as far back as 2019, 32% of the participant companies were deemed “RPA leaders”, meaning these financial institutions had accomplished the switch to RPA in virtually all basic functions. And yet this is just the tip of the iceberg; the benefits and ramifications of employing RPA in finance extends and bifurcates into such varied territory that it could creep up on newcomers, and in view of the cacophony around the topic, a reintroduction to the world of RPA would help thwart a few myths surrounding its application.

Here is a selection of the benefits offered by Robotic Process Automation in the banking sector.

1) Minimal initial capital demands

Since the concept of bank automation is alien to many, it is important to tackle the issue at the rudimentary level. Since RPA is one of the simpler branches of Artificial Intelligence (in conjunction with Intelligent Automation), the existing IT infrastructure of most established banks prove sufficient to reap the benefits; the financial institutions consequently have a very low risk-reward ratio. Though banks are not always up to date on their IT resources, the legacy systems of these banks can take care of most RPA implementations. Since these services are outsourced, little to no skill is required in operation; this mitigates the need of extra manpower and thereby, extra costs.

2) Scalability

Although this may be viewed as a follow on from the first point, it is worthy of mention. RPA bots are built to be stable and vastly scalable. Once the firm identifies its objective clearly (reduce cost or labor, saving time and so on), scaling of the available technology becomes smooth. In fact, when powered by the faculties, they can handle leviathan volumes of organized data: for instance, consider the issue of data consistency that financial establishments have to maneuver; like the ever-changing details in personal information held in the databases of banks, like the spelling of a person’s name, their address or financial details. While a slight variability can make conventional systems go for a toss, the empowered Robotic Process Automation systems can process such information with no hassles, thanks to its prodigious, yet rigid order. This is what makes RPA head and shoulders above the rest and key to its scalability is the fact that its machine learning features, when incorporated, are resilient and robust.

3) Speeding up processing

Speed and efficiency are synonymous for finance companies since customers need timely service for their requirements and delays nurture the risk of losing customers. When opening an account, for example, the verification process can be tedious for many. With RPA, the procedure can be expedited and accelerated exponentially. The robots can deal with everything from the validation to the delivery of the account to the client on completion. The same could be said on loan and mortgage applications, or the repetitive aspects of AP accounting, which when carried out by machines, frees up the employees to garner complex solutions involving reasoning and creativity. Thus, by optimizing performance and eradicating human error, RPA offers us a win-win; banks can take the adage of “time is money” literally and enjoy the reward of satisfied customers. Rread Experion’s success story here.

4) Promote intelligent banking strategies

The application of RPA in finance could bring in key foresights and vision into banking. Many of the top companies today are using AI analytics to procure a bird’s-eye view of the market, tracking unexpected changes and shifts in trends to offer their customers the most optimal investment options, or in fixing the most competitive interest rates; the role played by RPA in this regard is monumental. This is reassuring for customers as they offer maximum output with little risk.

While we are on the topic of risk, it is imperative to discuss the uses of RPA in fraud detection and safety. According to Thomson Reuters, banks spend almost $384 million on KYC process compliance every year on average, and many are moving to RPA to analyze customer data. Software bots run checks on customer data to assess and confirm KYC compliance, or to ascertain a customer’s credit score, which helps to keep malpractice at bay. If suspicious activity is intercepted, they can employ account freezes or other means to curtail frauds.

In conclusion
In an age where AI is growing and proliferating at a rate unseen hitherto, banks find themselves in a do-or-die situation, where adopting superlative technology is necessary to stay in the hunt. With the current rates of adoption of RPA in the finance realm, if banks wish to stay relevant and competitive, they must critically analyze their position in the market and make paradigm shifting changes in their strategy. They must be on the lookout for the top IT service providers to be ahead and make impactful strides in the industry, the effects of which will show us the way to the future.