Press Releases

Latest Press Releases

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.
FinRegLab attended ICML’s Responsible Decision Making in Dynamic Environments workshop and presented our work on a subsection of our white paper. The workshop poster and presentation focused on how model diagnostic tools affect lenders’ ability to manage fairness concerns related to identifying less discriminatory alternative models used to extend credit.
FinRegLab, in partnership with the U.S. Department of Commerce, the National Institute of Standards and Technology (NIST), and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), are hosting a symposium, bringing together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors, with a particular focus on financial services and healthcare.
The Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency released a guide for community banks on conducting diligence of financial technology companies. Drawing on existing guidance that articulates risk management expectations for third-party relationships, the guide highlights areas where required diligence processes can be adapted to reflect constraints related to doing business with early or expansion stage companies.
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.