FinRegLab is assessing the potential value of conducting empirical tests and other research to analyze particular data and technology solutions for identity proofing and transaction monitoring to improve financial inclusion and combat bad actors.

FinRegLab is launching a ground-breaking comparison of the financial inclusion benefits of using machine learning underwriting models with and without cash-flow data to increase responsible access to credit for consumers who may otherwise find it difficult to obtain safe and affordable loans.

FinRegLab is investigating ways to improve credit access for minority-owned companies and other underserved small businesses through the use of non-traditional data sources and mission-based lenders.

FinRegLab is investigating the financial inclusion and consumer protection implications of using new data sources for credit underwriting of micro and small enterprises (MSEs) in Kenya with a particular focus on women-owned small businesses.

Utility, telecom, and rental payment history can help to assess how credit applicants manage housing and other recurring expenses. FinRegLab and the Urban Institute have examined available research, historical and recent initiatives to increase data access and use for credit underwriting, and market and policy issues that will determine whether such efforts can reach scale.

FinRegLab presented a research proposal for research on the use of federated machine learning in Bank Secrecy Act/Anti-Money laundering compliance to the Central Bank of the Future Conference, which was hosted by the Federal Reserve Bank of San Francisco and the University of Michigan’s Center on Finance, Law & Policy. The 2020 edition of this conference explored the changing role of central banks and their potential to foster more inclusive economies in the United States and around the world. We also convened a panel conversation to highlight the potential inclusion and efficiency benefits of using federated learning for BSA/AML.

FinRegLab worked with a team of researchers from the Stanford Graduate School of Business to evaluate the explainability and fairness of machine learning for credit underwriting. We focused on measuring the ability of currently available model diagnostic tools to provide information about the performance and capabilities of machine learning underwriting models. This research helps stakeholders assess how machine learning models can be developed and used in compliance with regulatory expectations regarding model risk management, anti-discrimination, and adverse action reporting.

FinRegLab is working with teams at The Ohio State University and Charles River Associates to evaluate new workout structures and data and technology applications for consumers who are struggling with unsecured credit. The project will use data from pilots organized by the National Foundation for Credit Counseling and other sources.

Rapidly adjusting credit reporting and underwriting practices and processing small businesses’ Paycheck Protection Program applications posed substantial challenges in the first months of the pandemic. In 2020, FinRegLab produced a series of research briefs highlighting emerging issues and innovations.

Records from consumers’ deposit and card accounts and from small businesses’ accounting software can provide a relatively detailed and comprehensive picture of how applicants manage their finances on an ongoing basis. FinRegLab conducted an empirical assessment of the data’s benefits and risks, as well as market and policy analyses of the challenges to its wider adoption.