The home buying market has completely transformed in the last two years, with growing demand for bigger spaces, refinancing, and online-only application processes. Lenders that are equipped with the latest technology can gain from this period and create customer relationships that last the test of time. Yet, research suggests that mortgage servicing has run into severe problems.
In a 2021 report, the Consumer Financial Protection Bureau (CFPB) revealed that mortgage servicing complaints have reached a three-year high, and the CFPB received over 3,400 complaints in March 2021 alone. More than 38,000 borrowers complained about the origination and servicing process last year. How can lenders stem this tide, and make sure that the prospects who apply, stay? How do they deliver superior experiences that translate into repeat business, referrals, and lifetime relationships? The answer lies in the strategic implementation of AI.
In fact, artificial intelligence (AI) in mortgage can solve most — if not all — of the sector’s servicing challenges by reducing work, controlling errors, and maximizing availability.
The Role of Artificial Intelligence in Mortgage Servicing
Artificial intelligence (AI) helps automate repetitive processes by following business rules and learning from the exceptions to make outcomes better with every transaction. It also allows mortgage systems to process large volumes of unstructured data, such as social media information, chat conversations, text embedded in screenshots, etc. This allows lenders to collect information and interact with borrowers in new ways to enhance the servicing experience.
Unfortunately, the use of AI is typically restricted to the origination stage of loan processing. Since it is a data-heavy process, AI can easily perform most of the tasks usually actioned by human employees. While we agree that AI in origination has enormous potential, its application in servicing is equally important.
6 Ways AI Solves the Mortgage Industry’s Key Servicing Challenges
Lenders usually face the following six problems in the course of the servicing lifecycle, all of which can be solved through AI intervention.
1. Servicing is offshored to a best-cost country, but language capabilities are lacking
Due to the high volume of processes involved, lenders often share ownership of servicing with a third-party provider. While these external partners bring industry expertise and certifications, they are often situated in a best-cost country that’s different from the location of the lender and borrower. AI helps overcome language barriers through multilingual NLP and automated translation.
2. Monitoring is central to loan servicing but remains largely manual
A lot of the servicing efforts are spent not on interacting with the customer, but on monitoring outstanding debt. Lenders must regularly review loan performance, compliance with covenants, and adherence to loan terms — which is mostly performed by human analysts. These executives may be prone to mistakes, or they will take days to look over a single set of delinquency reports, rent rolls, balance sheets, etc. All of this can be automated through the use of AI.
3. Customers have to wait for days, weeks, or even months to resolve complex queries
The most long-standing complaint with mortgage servicers is probably their average waiting time. Borrowers expect to wait for several days (extending into weeks or months), to receive a response to a query. This is because a response may comprise unique data and a collection of reports, which someone has to painstakingly find and assemble. AI achieves this through a simple conversation flow.
4. When an expert resource leaves, their tribal knowledge leaves with them
The success of a servicing function often relies on a single team or a crack resource who has managed to establish an efficient system. But without technology, there is no standardization, and without AI, there is no way to institutionalize these efficiencies as part of the servicing process. AI makes it possible to document tribal knowledge and make it reusable for the future.
5. Incorrect reports are no longer an option in an evolving regulatory climate
Mortgage servicing rules are always subject to change, and the pandemic has proved how volatile the regulatory climate can truly be. For example, the state of New York recently amended servicing requirements for certain delinquent borrowers. AI makes it easier to keep up with changing regulations by simply configuring the business rules and training the machine learning model with new data.
6. Borrower expectations are changing, but established lenders are struggling to keep up
The new generation of borrowers is squarely digital-first, and this trend has intensified in the last two years. For instance, 3 in 5 borrowers are influenced by a mortgage company’s digital capabilities when selecting a lender. However, without AI, digital tools risk becoming fancy storefronts operated by the same legacy processes. Mortgage providers need AI technology to provide a servicing experience that is as good as their UX promise.
Still Stuck on an Outmoded Servicing Model? Talk to Our Tech Gurus
At Nexval, we develop cutting-edge artificial intelligence and machine learning solutions to help mortgage providers meet business outcomes. Our technology expertise combined with decades of experience in the mortgage servicing sector informs our tailored solutions. Right now, lenders are at the cusp of brand-new business opportunities, stemming from a new generation of home buyers and efficiency gains from AI and automation. Are you ready to unlock these benefits and grow your bottom line? Speak with Tech Gurus today.