How Artificial Intelligence and RPA Can Help Lenders Process Mortgage Applications Faster

Written by Brinda Sreedhar in Automation Anywhere News on April 25, 2019
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Why do mortgage applications take weeks, sometimes months? Digital-native customers, such as millennials, are using their mobile devices to order a ride, request food delivery, make international money transfers, and are now expecting the same level of speed when applying for a home mortgage loan. Mortgage lending institutions need to harness Artificial Intelligence (AI) to speed up responsiveness, shorten the time for mortgage applications, and keep an eye for fraud to protect both the lender and the borrower.

AI brings agility for faster mortgage financing

In an earlier era, customers only wanted the best product at the best price from their lending institutions, but speed was less important. Today, more millennials are pursuing homeownership than ever before. As millennials age and grow in their careers, they are acquiring more purchasing power, doing their mortgage shopping online, and entering the market well prepared. According to the National Association of Realtors, 30% of millennials purchased homes for $300,000 and higher in the past year in USA. This trend is only growing higher in 2019. Banks and mortgage lending institutions must address the digital nature of today’s buyers by adding AI-powered, intelligent Robotic Process Automation (RPA) to accelerate all bank processes that adapt and improve on their own to meet the customers’ needs. Due to the complex nature of mortgage processes, getting a home loan can take some time. Many of the lending organizations rely on manual processing of multi-page loan documents, pay stubs, W2s, and additional loan documents. Lending institutions must manually, and often painstakingly, extract information from these documents and input them into databases, such as ERP systems to be processed further.

Can technology make the home loan processes faster?

Implementing an agile AI and RPA system doesn’t mean embracing chaos. In fact, a system that’s well designed observes well-defined rules and ensures institutional safety. AI-powered RPA can help banks solve specific processing problems and increase productivity by 20%, freeing-up employees to focus on building relationships with customers.

According to National Mortgage News, 71% of all home loan lenders agree that technology will help them stand out from their competition. To process mortgage applications, banks today deal with complex legacy systems, disparate databases, and spreadsheets which involve lots of physical and digital documents. This is where AI combined with RPA can make banking more efficient at delivering home loans for qualified customers. RPA brings numerous opportunities for adding agility to multiple processes. Since RPA is rule-based, it can speed up loan eligibility verification based on pre-defined criteria that every application is evaluated for.

Can banks keep both eyes on risk?

We live in a world of risk that needs to be judiciously monitored. Mortgage lending requires constant risk assessment and compliance oversight. Automation streamlines operations and ensures safeguards are in place to monitor and report exceptions to avoid unwanted surprises to either banks or customers. Automation immediately reduces manual errors, such as keying mistakes, misapplied rules, etc. Risk monitoring and reporting also becomes easier to scale with RPA. The advantages of RPA are clear and immediate: agility in bank processes is better for customers and the staff, who are now able to focus on customer relations rather than on mundane, repetitive tasks. In combination, this improves efficiency while reducing operational costs. Here are some advantages to introducing automation in your ecosystem:

Banks can start with streamlining operations. Costs for origination and servicing are at an all-time high. AI technologies can help reduce loan defects, which impact the loan quality that increase lender costs. AI, specifically machine learning (ML), can also speed up ingestion of loan applications along with data extraction to significantly enhance customer experience. The extraction algorithms for AI can be customized to know what to look for in W2s, paystubs, banks statements, and the overall loan application. Without AI, automation has its limits. For example, processing loan application along with data extraction cannot be automated with RPA alone or even traditional optical character recognition (OCR) technologies due to the varied formats of documents. A W2 statement looks different from a bank statement and carries different weight in which data needs to be extracted. AI, in contrast, gives you the cognitive ability to scan and move data at a much faster rate for higher straight-through processing.

Detect fraud or reduce defaults. Incidents of fraud in mortgage applications are on the rise. According to CNBC, one in every 109 mortgage applications are estimated to have fraudulent claims. Due to the high volume of applications, this part of the process is most efficient when humans work in collaboration with AI technology to detect fraud. This is another area where the combination of RPA with AI can have a direct impact on reducing the number of fraudulent claims. Banks can use historical data to create machine learning models that can predict the probability of fraud or indicate if a particular loan application has the possibility to default. AI and deep learning are effective in performing real-time data analysis to weed out high-risk applications based on factors such as historical data to determine the validity of an application. Banks can now have the swiftly detect problems and do something about them.

Augment Mortgage servicing with AI. Mortgage servicing is one of the longest-lasting aspect of the mortgage process in terms of user interactions because it involves receiving checks for the loan lifecycle. Many banks are using legacy systems for invoice processing and claims management. They also don’t have good processes for effectively dealing with delinquent customers. From the customers’ perspective, it’s frustrating to not get fast, reliable information about their mortgages. In this context, AI can be useful for the bank’s invoice processing, giving skilled workers valuable time back to service their customers and only needing to look into exceptions.

Delighting your customers with award-winning service. The full cycle of mortgage lending is complex. It includes gathering applicant data, credit validation, underwriting, drawing loan documents for the servicer, onboarding, billing, and finally collecting the monthly payments. Building a seamless experience for the customer requires you to approach your process from the digital customer’s perspective and meet their needs for speed and service.

The rise of the digital customers—accustomed to Netflix, Uber, Instagram, and Google—are getting banks to implement an complete agile system, from mortgage qualification to closing. Leaders in banking who are adding AI and RPA are making their customers’ journey faster, more accurate, and effective while building brand loyalty for retaining their top end customers for the lifetime of the loan. Automating manual processes is in many ways helping humans be vast and brilliant in living their home ownership dream.