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Fed flags risks of AI in fair lending
Federal Reserve Vice Chair for Supervision Michael Barr in a speech Tuesday flagged several fair lending risks associated with using artificial intelligence (AI) technologies. Barr’s remarks were consistent with past observations made by federal financial regulators, including the NCUA and CFPB, as they consider guidance and regulations related to these technologies.
Barr noted while AI techniques, such as machine learning, “have the potential to leverage these data at scale and at low cost to expand credit to people who otherwise can’t access it…they also carry risks of violating fair lending laws and perpetuating the very disparities that they have the potential to address.”
“Risks are amplified when a model is opaque and lacks a sufficient degree of explainability – the degree to which the bank can understand how data, variables, and other features inform the credit decisions,” he said.
Federal financial regulators sought feedback on AI use in 2021. NAFCU shared how “[c]redit unions are leveraging AI to support a variety of operational needs to better deliver safe and affordable services to their members,” including expanding access to high-quality credit among lower-income populations, while calling for “a supervisory approach that does not add to already high examination burden.”
Barr also discussed appraisal bias and the risk of deficient collateral valuations, noting that these “can contain inaccuracies because of errors, omissions, or discrimination that affects the value of the appraisal, and the reconsideration of value may help to properly value the real estate.”
The NCUA and other federal financial regulators currently have two related proposals open for comment on automated valuation models (AVMs) and reconsiderations of value (ROVs).
NAFCU is supportive of pathways for responsible innovation that allow credit unions to innovate and fully comply with Equal Credit Opportunity Act (ECOA) and other fair lending laws.