FinRegLab is extending its investigation of the adoption of artificial intelligence in financial services through a policy analysis focused on the growing use of machine learning models for underwriting credit and a January 17 webinar with senior federal financial regulators to discuss generative AI and other recent developments.
Machine learning models are being used to evaluate the creditworthiness of tens of thousands of consumers and small business owners each week in the U.S., increasing the urgency of answering key questions about their performance, governance, and regulation.
FinRegLab is working with researchers from Stanford Graduate School of Business to launch a ground-breaking evaluation of emerging market practices to improve the transparency and fairness of machine learning underwriting models in consumer credit.
Resolving data transfer issues could facilitate use of cash-flow data, with particular opportunity to expand access to credit for millions of underserved consumers and small businesses
Data is being used by both new entrants and traditional lenders to extend smaller loans to smaller businesses and increase credit to underserved communities.
Data both helps to underwrite applicants who lack traditional credit history and improves risk sorting among borrowers who are ranked similarly by traditional scoring systems.