As the Senate Banking Committee examines the growing role of artificial intelligence in the U.S. economy, America's Credit Unions is urging lawmakers to adopt a balanced regulatory approach that encourages innovation while avoiding unnecessary burdens on community-based financial institutions.
In a letter submitted ahead of Wednesday's hearing, "AI and the American Dream: Promoting Innovation, Affordability, and American Dominance," the trade group highlighted how credit unions are already deploying AI across lending, fraud prevention, compliance and member service operations.
According to the letter, credit unions are "leveraging the power of AI safely and securely to help meet the financial needs of their members," using the technology for tasks ranging from underwriting assistance and fraud detection to automating previously labor-intensive processes and accelerating loan decisions.
The organization also emphasized AI's growing role in fraud prevention, noting that some institutions are using predictive models and machine learning tools to identify suspicious transactions, check fraud and account takeover schemes before losses occur.
At the same time, America's Credit Unions cautioned regulators against overly prescriptive oversight. The letter argues that requiring detailed reviews of source code, algorithmic weights or proprietary models would create "significant and impractical burdens on credit unions that rely on proprietary models from third-party vendors" and could divert resources away from compliance and innovation efforts.
Instead, the trade group called for principles-based regulation, writing that "regulatory assessments of new technologies must embrace balanced and flexible approaches to risk management that can accommodate innovation while protecting consumers."
The letter also pushed back on proposals to require institutions to fully deconstruct AI algorithms for fair-lending reviews, arguing that existing compliance tools, including self-testing, loan file reviews and HMDA analysis, already provide effective methods for identifying discrimination risks.
"AI and machine learning can improve access to credit and access to housing," the organization wrote, adding that credit unions can use the technology to identify overlooked borrowers while maintaining appropriate human oversight.
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