Consider the massive size of real estate lending. The Fed’s latest report shows mortgage debt topping $9 trillion. When including mortgages from businesses, it tops $15 trillion. Over 10 million homes and commercial properties sell each year.
Equally staggering is how much data exists on the borrowers. Lending is big business and big data, and while banks are harnessing that data, private lenders now need to follow suit.
Powered by the rise in computer processing power and individual data, artificial intelligence can find patterns that predict borrower behavior, helping lenders make more money. AI is not quite the stuff of movies like Terminator. Think of it as software analyzing statistics at scale.
The use of machine learning to analyze alternative data in loans and credit rating is going to raise some privacy, ethical, and legal concerns. Many people might not feel comfortable with a company having access to all of this sensitive information about their life. Even if all these companies behave ethically, the more data they hold the more that can be stolen by malicious hackers in a data breach.
The use of “big data” also runs the risk of companies accidentally or purposely discriminating against groups. For example, a program might not deny applications from protected minorities, but it might deny applications for individuals who have a dozen data markers that just happen to highly correlate with those groups.
Even with these concerns the use of machine learning to process alternative data to determine creditworthiness is likely to grow significantly. There are billions of people without real credit histories that companies may one day want to offer mortgages, payment plans for products, credit cards, or other loans. The financial appeal of these tools is obvious. It is reasonable to believe that the more information you gather about an individual, the more likely you would be able to predict their behavior, including how diligently they would pay back a loan.