/AI Can Make Bank Loans More Fair

AI Can Make Bank Loans More Fair

Summary: As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending” unfairness

Original author and publication date: Siam Townson – November 6, 2020

Futurizonte Editor’s Note: Will AI move us beyond discrimination and unfair practices? Probably not. Why? Read below.

From the article:

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.

The problem of bias is an endemic one, affecting financial services start-ups and incumbents alike. A landmark 2018 study conducted at UC Berkeley found that even though fintech algorithms charge minority borrowers 40% less on average than face-to-face lenders, they still assign extra mortgage interest to borrowers who are members of protected classes. Recently, Singapore, the United Kingdom, and some European countries issued guidelines requiring firms to promote fairness in their use of AI, including in lending. Many aspects of fairness in lending are legally regulated in the United States, but banks still have to make some choices in terms of which metrics for fairness should be prioritized or de-prioritized and how they should approach it.

So how can financial institutions turning to AI reverse past discrimination and, instead, foster a more inclusive economy? In our work with financial services companies, we find the key lies in building AI-driven systems designed to encourage less historic accuracy but greater equity. That means training and testing them not merely on the loans or mortgages issued in the past, but instead on how the money should have been lent in a more equitable world.

The trouble is that humans often cannot detect the unfairness that exists in the massive data sets that machine-learning systems analyze. So lenders increasingly rely on AI to identify, predict, and remove the biases against protected classes that are inadvertently baked into algorithms.

READ the complete original article here