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  • Joseariel Gomez, CEO, Shastic

AI: The Next Step in RPA

Guest Editorial by Joseariel Gomez, Chief Executive Officer, Shastic

Lenders and financial institutions are always looking for ways to become more efficient. The lending market is insanely competitive, and efficiency separates the winners from the losers. Even small efficiencies can add up and massively grow a business. Robotic process automation (RPA) is just one tool that many lenders use to increase efficiency. RPA is great for many reasons: RPA uses digital tech to make processes faster and frees up knowledge workers to work on other tasks. Manual processing typically creates delay after delay, automation brings us closer to real-time lending.

Joseariel Gomez

With that being said, traditional RPA isn’t perfect. Actually, many financial institutions have had a lot of problems with RPA. RPA requires heavy use of IT resources and often can’t work across existing systems. In fact, with the extremely high volume of activity and data that lenders face, RPA is sometimes slower than if a human did the same job. Obviously, this is not sustainable.

Lenders can’t just take care of customers’ current needs to stand out from the competition. Lenders must be proactive and anticipate borrowers’ future needs, but figuring out those needs requires processing massive amounts of data in real-time. Traditional RPA actually isn’t great at this. RPA can do one repetitive task very well but isn’t much use for processing information.

Luckily, artificial intelligence (AI) has revolutionized RPA. AI technology is the next step for RPA and can solve the problems with typical RPA. AI is flexible, powerful. RPA is used across many industries, but they have their greatest potential in financial services, especially mortgage lending and consumer lending. AI is ideal for taking a lot of information and choosing the best next steps in real-time, so RPA with AI integration both selects and executes tasks that best serve the customers. The Federal Reserve is expected to raise interest rates in 2022, but with low interest rates and high application volume, RPA is the perfect solution for mortgage lenders who want to increase efficiency.

Many lenders and other financial service employees spend most of their time doing the same tedious, time-consuming tasks like contacting customers or retrieving documents and signatures. These are all important functions, but it leaves very little time for approving loans and engaging with customers. RPA automates the most time-consuming tasks, so knowledge workers can spend their time on more profitable activities. The Mortgage Reports states that the mortgage process takes an average of 30 days to complete, and RPA has been shown to improve processing time by 33%. RPA can contact borrowers, retrieve documents, secure signatures and monitor workflow. AI fits into this by figuring out what information it needs, who it needs to contact and when it needs to contact them.

RPA can also improve engagement between lenders and borrowers. With so much competition, borrowers pick lenders that communicate effectively and without much hassle. It’s the lenders job to make communicating seamless and effective, and lenders that process communications faster will receive more business. However, constant communication is expensive, and lenders deal with high volume, which makes good communication difficult. RPA can send the right message to the right person at the right time, and AI can figure out how to do that without any extra work.

Lenders deal with granular details and overwhelming volume. It’s a demanding industry, which is partly why so many fintechs have hit the scene to make those processes easier. RPA is just one of the latest tools for financial institutions to increase efficiency. Efficiency drives growth for financial institutions because lenders that save here and there can give their customers and members more. Lenders can offer more competitive rates in a crowded market and approve more loans. However, increasing efficiency isn’t easy. Deciding how to increase efficiency is already challenging enough, then implementing those solutions is often more difficult and expensive. RPA is a good tool for increasing efficiency in some ways, but it has its flaws. Traditional RPA struggles with high volume and working across systems and departments. They often aren’t designed specifically for financial services, so they sometimes eat up more resources than they make available.

However, AI can fix all of these problems. AI can work across systems, so it’s capable of doing more and doing it faster. AI and machine learning can also handle the high volume that financial institutions deal with. While there will surely be more innovations to come, AI is the next step in the evolution of RPA.

Joseariel Gomez is Chief Executive Officer of Shastic (, an RPA-as-a-Service provider for financial institutions.

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