Machine learning

Artificial intelligence systems built to assess creditworthiness are trained on data that implicitly accepts past discriminatory lending decisions as legitimate signals about borrowers today.

While the risks, benefits and magnitude of artificial intelligence's impact on financial services remain unclear, agencies should keep an open mind toward the technology and avoid reflexive risk aversion in bank supervision, Federal Reserve Gov. Michelle Bowman said.

Acting Comptroller of the Currency Michael Hsu said that while artificial intelligence is a potentially transformative technology, regulators should be wary of waiving liability for its use in banking.

BofA had nearly 1,100 artificial intelligence and machine learning patents and pending applications within its portfolio at midyear, and more than half have been granted already.

Generative AI is exciting, but has limited fraud-busting utility. Instead of rushing to implement gen AI-powered anti-fraud initiatives, banks should focus on strengthening their existing security measures.

Witnesses from companies across financial services and housing, including a credit union and a core services provider, attested to the value of AI in fighting fraud and boosting customer service at a hearing in the House of Representatives.

All cultures have explicit and implicit biases. Preventing discrimination when large language models are involved starts with using high-quality human-curated data and text.

AI tools, such as large language models, offer the promise of increased efficiency; but they also carry with them potential legal and regulatory risks. Financial service providers will need to find a way to strike the right balance as technological advancements continue to emerge.

In recent years, the wealth gulf between white and Black families has only grown. But there is hope that advances in fintech, such as wider adoption of artificial intelligence, can help.