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As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions’ choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right?
Sadly, what’s true in theory has not been borne out in practice. Lenders often find that artificial-intelligence-based engines exhibit many of the same biases as humans. They’ve often been fed on a diet of biased credit decision data, drawn from decades of inequities in housing and lending markets. Left unchecked, they threaten to perpetuate prejudice in financial decisions and extend the world’s wealth gaps.
Lending is a massive business in the United States which directly and indirectly touches almost all parts of the economy. With tens of millions of Americans holding loans worth trillions of dollars, any technology that can make even a small improvement in a company’s returns on the loans they hold, or that can improve their share of the market, would be worth a significant amount of money.
That is why both established banks and startups in the field are constantly looking for ways to innovate – and artificial intelligence might allow for just that. In fact, our AI Opportunity Landscape research shows that approximately 15% of the venture funding raised for AI vendors in the banking industry is for lending solutions.
At its core, lending is a big data problem, making it a business naturally suited for machine learning. Part of the value of a loan is tied to the creditworthiness of the individual or business that took out the loan. The more data you have about an individual borrower (and how similar individuals have paid back debts in the past), the better you can assess their creditworthiness.
The value of a loan is thus tied to assessments of the value of the collateral (car, home, business, artwork, etc…), the likely level of future inflation, and predictions about overall economic growth. The promise of AI is that theoretically it can analyze all of these data sources together to create a coherent decision.