Mortgage lending is a data-intensive business. That’s obvious to anyone who has applied for a home loan. Among the documents you need to hand over are two years’ worth of tax returns, W-2s, bank statements, copies of identification plus any name changes, proof of other income, and so on. As laborious as it is for borrowers, all that paperwork requires a great deal of time and effort on the other side of the lending process.
Mortgage lenders need to assimilate, sort, evaluate and weigh all of that information to ensure the loan will be repaid. At an average mortgage of $260,386, there’s a lot at stake.
That’s why I was intrigued by a recent survey of mortgage lenders and their use of artificial intelligence (AI) and machine learning (ML) in lending. The survey found that the use of these technologies in the mortgage industry is going to boom over the next two years. And that will be a good thing for borrowers and the market.
Fannie Mae, one of the federally sponsored agencies that back mortgages, surveyed senior mortgage executives at 184 lending firms on their interest in AI/ML last year. The survey found that while nearly two-thirds of lenders are familiar with AI, only about one quarter (27%) are using it in their businesses now — and only half of that group are currently using it with customers (the rest are doing trials).
Yet when asked to look ahead two years, fully 58% of lenders expect to be using AI/ML in their mortgage business. Of the rest, 22% predict they’ll be investigating AI, and 19% foresee being in a “wait and see” mode. Only 2% of lenders stated outright they wouldn’t be using AI. The divide between the low number of banks using AI/ML right now and the number who expect to be deploying it in two years shouldn’t come as a surprise.
AI is just starting to make inroads. While AI voice assistants such as Alexa and Siri may be part of our everyday world, more sophisticated AI is only beginning to make its way into industries such as health and finance, where there’s more at stake than your Alexa adding toothpaste to your shopping list. With financial information and loans, lenders and regulators rightly have an explicitly regulated duty to ensure these new technologies are used appropriately. AI and ML systems need to be effective.
They also need to be transparent about making decisions about why applicants are denied or get different mortgage rates than others. But AI and ML still have enormous potential for the mortgage space. For one, a tremendous amount of effort goes into just organizing and reading all the paperwork. Analyzing those documents can be managed far more efficiently with computers, freeing up lending professionals to use their experience and judgment on individual applications—a plurality of lenders, 42%.
I told Fannie Mae their primary objective with AI/ML is to improve operational efficiency. Nearly as many, 41%, say they expect these technologies will improve the borrower experience. For instance, while right now you could expect to wait about three weeks for a mortgage application to be approved, AI will probably reduce that to one day within a few years.
Bankers also envision smart online applications, where interview questions are responsive to your specific answers. These methods shorten the basics of the process to focus more on your qualifications and needs. The bigger picture is that AI/ML can better evaluate the ability of people to repay their mortgages. This should open up loans to a wider variety of people who are good bets to repay a mortgage but for whatever reason – typically lack credit history – would be rejected today. ML can literally take thousands of data variables.
Through its advanced mathematics, find hidden correlations that better indicate whether someone will repay their loan. All that insight allows lenders to view borrowers as more than the handful of numbers in traditional lending models. Basically, AI/ML gives them a fuller picture of applicants as people. And history suggests lenders need better tools. It’s worth remembering that at the height of last decade’s mortgage boom, several lenders faced with a high volume of loan requests streamlined their process down to using just one variable – three-digit credit scores – to decide on applications.
While the financial crisis that followed had many complex causes beyond that fact, relying on one useful variable as the sole determination of risk was a mistake. Experience with ML shows that people with the same credit score can be, in fact, less (or more) risky than each other when additional pieces of information are used in the lending process.