We discuss the evolution of anti-discrimination oversight with leaders of BLDS, LLC, which has since created Solas AI.

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.

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.

America’s credit system is under serious pressure as it faces the most sudden and severe downturn since the Great Depression. Our CEO Melissa Koide and former President and CEO of FICO Larry Rosenberger released an op-ed titled “Data needs to be wider-sourced and more inclusive” discussing using more financial data for lending, enhancing public policy guidance, and financial exclusion and the COVID-19 effect.

We recognize the breadth of urgent issues facing the Consumer Financial Protection Bureau and the nation at this time, but believe that resolving critical questions about access to financial data would substantially benefit consumers, small businesses, and financial services providers in helping to recover from the Covid-19 pandemic, address longstanding racial wealth gaps, and make U.S. financial systems more generally inclusive, competitive, and responsive to customer needs.

The Consumer Financial Protection Bureau has issued an advisory opinion concerning the establishment of special purpose credit programs to meet the needs of underserved groups consistent with the Equal Credit Opportunity Act. It is the Bureau’s first action following on a Request for Information about ECOA issues earlier in 2020.

FinRegLab presents a webinar with diverse leading voices from the federal regulatory community on the use of artificial intelligence in credit underwriting. The event focuses on regulators’ perspectives in relation to explainability and fairness in consumer and small business credit underwriting models where all credit stakeholders – lenders, advocates, and policymakers – are considering the implications of a broad transition to AI-based underwriting.

FinRegLab proudly presented a proposal for research on the use of federated machine learning in BSA/AML 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.

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.

This update catalogues recent initiatives involving the use of non-traditional credit data, including cash-flow information. It considers how dramatic shifts in economic conditions due to the Covid-19 pandemic and mass movements for racial justice have increased incentives to adopt new data sources and models, but also created new market and policy challenges.