How Machine Learning Is (And Isn't) Changing Fair Lending

Our CTO Jay Budzik had the great good fortune to participate in a discussion focused on AI and machine learning at the Fair and Responsible Lending Forum during the CBA Live conference (virtual edition) this past month. Joining Jay in the discussion were Michaela Albon, senior vice president and general counsel for TIAA Bank's consumer and home lending and head of its fair and responsible lending practices, Stephen Hicks, who runs Bank of America's enterprise fair lending group, and Stephen Hayes, a partner at Relman, Dane & Colfax, practicing in civil rights and litigation with an emphasis on fair lending and fair housing, consumer protection, compliance, and fintech issues. The discussion kicked off by defining machine learning and laying out the benefits and challenges of using ML models in underwriting. Machine learning is a computing technique that makes predictions based on patterns observed in data. ML algorithms are used in a variety of ways in banking (think about marketing automation, chatbots, document scanning and analysis), but in lending specifically, they're used to predict the likelihood of a loan getting repaid or going bad. "The reason people are excited about machine learning," says Zest's Budzik, "is because it's more effective at identifying those applicants that are likely to default."