FinRegLab has launched a new research project using data from the National Foundation for Credit Counseling (NFCC) to evaluate ways to help consumers recover more quickly from personal and economic crises such as COVID-19. The project will analyze pilot initiatives by nonprofit counseling agencies and other data sources as a springboard for considering broader market and policy changes.
“Artificial intelligence and machine learning analyses are driving critical decisions impacting our lives and the economic structure of our society. These complex analytical techniques—powered by sophisticated math, computational power, and often vast amounts of data—are deployed in a variety of critical applications, from making healthcare decisions to evaluating job applications to informing parole and probation decisions to determining eligibility and pricing for insurance and other financial services.”
FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business have released new research on “Machine Learning Explainability and Fairness: Insights from Consumer Lending.”
Testimony & Comment Letters
FinRegLab Responds to Comments on Proposed Third-Party Relationships Guidance
Coordinated action is critical between federal regulators to continue moving the growing ecosystem for customer-directed transfers toward adoption of safer technologies and practices without undermining consumers’ § 1033 rights or frustrating the law’s potential benefits for competition and innovation.
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.
“I believe we’ve reached a crossroads in consumer finance, where things will probably get either much better, or much worse, due to the technological transformation of products, providers, cost structures, business and market models, and infrastructure.”
FinRegLab’s forthcoming research will help to inform the extent to which current laws and regulations are able to be satisfied in light of the emergence of more complex underwriting models, how well tools to develop and monitor those models perform in identifying effective ways to pursue greater inclusion and fairness, and considerations for policy and market developments that can support the safe, inclusive, and nondiscriminatory adoption of machine learning.
Testimony & Comment Letters
FinRegLab’s Testimony to the Housing Financial Services Committee’s AI Task Force
FinRegLab CEO Melissa Koide testified in the Task Force’s hearing on “Equitable Algorithms: How Human-Centered AI Can Address Systemic Racism and Racial Justice in Housing and Financial Services.”
In many smaller American towns banks and credit unions are finding usual sources of loan demand dwindling — and that was before the COVID-19 recession. Community banking institutions may find trouble if they market their credit services further afield. The solution may be to dig deeper for loans in the communities they already know, marketing loans to be evaluated with new alternative data sources (like some fintechs do).
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