The mortgage industry requires numerous professionals—such as loan officers, underwriters, etc.—to work together to complete a wide variety of processes to approve a loan. All while, consumers increasingly expect faster turnaround times on closings, putting a lot of pressure on mortgage professionals to get the job done quickly. So the more access to helpful tools for increasing efficiency, the better! That’s where robotic process automation (RPA) comes in.
The fact is that many of the processes involved in the mortgage industry are repetitive, relatively simple, and need to be done in a specific order—making them perfect candidates for automation. With robotic process automation, RPA software takes over routine processes in which simple tasks need to be completed the same way consistently, quickly, and accurately.
The use of RPA in the mortgage industry can increase efficiency and speed while reducing expenses and the risk of errors. So, how exactly does robotic process automation work in the mortgage industry, and what can we expect from it in the future? Take a look at some use cases of mortgage support automation platforms like Capacity to get an idea of how RPA in the mortgage industry is changing lending.
Faster, more intuitive workflows.
Successful mortgage lending demands reliance on several processes from the beginning to the end of the workflow. Every task is critical and a unique part of the lending process, so leaving any out to save time and satisfy the borrower isn’t an option. But automating some tasks is, and that’s what some mortgage lenders are increasingly choosing to do. This improves efficiency and reduces the opportunity for user error in the mortgage industry.
Some examples of processes that can be automated include data entry, email notifications, and document routing. Basically, any workflow processes that are rules-based, highly repeatable, and low in complexity are good candidates for RPA. Any loan professionals interested in seeing how RPA in the mortgage industry is changing lending should look to automation platforms like Capacity.
Expedited loan processing.
Borrowers want to get through the lending process as soon as possible, but it takes 45 to 60 days on average to close on a loan. The only way to reduce the loan processing time would be to speed up each task along the way. Hiring more employees can help, but that hikes up the costs for lenders. And rushing the steps of the lending process can lead to errors that have huge consequences for lenders and borrowers alike. That’s why lenders are learning to turn to RPA to expedite loan processing.
Automating this procedure allows lenders to process more loans faster than usual, improving productivity and customer satisfaction. After all, AI-powered mortgage support automation platforms like Capacity can connect to numerous apps that lenders use to determine loan eligibility. For example, with Capacity, loan officers have instant access to Freddie, Fannie, FHA, VA, and USDA guidelines. They can also get quick answers to common questions as they work hard to close on loans, allowing them to move through the loan origination process seamlessly, which benefits both employees and customers.
PRMG is one example of a mortgage lender that has seen direct benefits from Capacity’s ability to provide employees instant answers to questions when processing loans. In fact, this platform deflects over 950 questions per week, equipping employees with the accurate information they need to go through the lending process quickly.
Better borrower experience.
Another way we can see how RPA in the mortgage industry is changing lending is by improving the experience borrowers have. And this is critical since one study found that 96 percent of consumers will switch to a different company after a bad customer experience. So, what does a good customer experience consist of? Generally, consumers want a clear, simple loan application process and fast answers any time they have questions. Fortunately, RPA makes this possible.
Of course, the move to RPA is not without potential challenges. For instance, not every process can be automated, as unstructured data and varying data formats can make automation difficult or even impossible. However, automating even a portion of tasks in the mortgage industry can make a drastic difference in the team’s productivity, as employees can process more loans faster than they could before RPA. This will lead to greater customer satisfaction and company profitability.
