process-mortgage-apps-faster-with-rpa

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

Why do mortgage applications take weeks and sometimes months? Digital-native customers, such as millennials, are using their mobile devices to order rides, request food delivery, and 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 accelerate responsiveness, shorten the mortgage application process, and protect both the lender and the borrower from fraud.

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 home ownership than ever before. As millennials age and grow in their careers, they're acquiring more purchasing power, shopping for mortgages online, and entering the market well prepared.

According to the National Association of Realtors Research Group, 25% of millennials purchased homes for $250,000 and higher in the past year in the U.S. And this trend is only growing 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 and the customer experience.

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 multipage 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 enterprise resource planning (ERP) systems to be processed further.

Can technology make the home loan process 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 employees to focus on building relationships with customers.

According to National Mortgage News, 72% of all home loan lenders agree technology will help them stand out from their competition. To process mortgage applications, banks today deal with complex legacy systems, disparate databases, and spreadsheets, all of 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 accelerate loan eligibility verification based on predefined 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 become easier to scale with RPA.

The advantages of RPA are clear and immediate: Agility in bank processes is better for customers and staff, who are able to focus on customer relations rather than mundane, repetitive tasks. In combination, this improves efficiency while reducing operational costs. Here are four advantages to introducing automation in your ecosystem:

  1. Banks can start 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 increases 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, pay stubs, banks statements, and the overall loan application.

    Without AI, automation has its limits. For example, processing a loan application along with data extraction can't 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.

  2. Detect fraud or reduce defaults

    Incidents of fraud in mortgage applications are on the rise. According to CNBC, about one in every 109 mortgage applications has 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 ML 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 swiftly detect problems and do something about them.

  3. 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 customer perspective, it’s frustrating not to get fast, reliable information about their mortgages. In this context, AI can be useful for the bank’s invoice processing, giving skilled workers — who only need to look into exceptions — valuable time back to service their customers.

  4. Delight your customers with award-winning service 

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

The rise of digital customers — accustomed to Netflix, Uber, Instagram, and Google — is leading banks to implement a complete agile system, from mortgage qualification to closing. Leaders in banking who are adding AI and RPA are making their customer journey faster, more accurate, and effective while building brand loyalty for retaining their top-end customers for the lifetime of the loan.

See how mortgage loan automation
delivers faster approvals.

SHARE THIS: