Be prepared to explain ‘explainability’ of AI
The increasing use of AI in evaluating and extending credit is reanimating long-standing concerns about fairness and transparency in lending. All too often, AI decision-making can resemble a "black box," frustrating borrowers and regulators alike. The CFPB recently highlighted the need for "explainability" in lending decisions – but what does that mean in practice?
The banking industry’s record of discrimination in loans and credit has not been pretty.
Repeated studies have found that Black and Latino Americans are much more likely to be unbanked or underbanked than white Ameicans, and that these populations continue to express distrust in banks and financial institutions.
Today, it is assumed that most respectable financial institutions do not intentionally discriminate against consumers based on race, gender, religion etc. — and this has been illegal for half a century under the Equal Credit Opportunity Act.
In one sense, any gaps actually represent missed opportunities for banks and credit unions to reach these communities. Nonetheless, financial models today are so complex that it is possible to generate discriminatory outcomes without anyone intending to do so. And so the gaps also represent potential liability for banks not prepared to enter the regulatory minefield.
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