This report surveys the options that are available to consumers who are struggling with unsecured debt, including available research on the options’ scope and outcomes. It provides an overview of market and policy challenges as stakeholders ponder strategies to help consumers recover more quickly from personal and broader economic crises such as COVID-19.

This executive summary highlights key themes from our longer market and policy context report, which surveys the options available to consumers who are struggling with unsecured debt and market and policy challenges to helping consumers recover more quickly from financial shocks.

FinRegLab partnered with the Urban Institute to detail twenty years of research and efforts to access utility, telecom, and rental payment history for credit scoring and underwriting. The paper describes recent initiatives and key challenges going forward.

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.

This report surveys market practice with respect to the use of machine learning underwriting models and provides an overview of the current questions, debates, and regulatory frameworks that are shaping adoption and use.

This overview highlights key themes from our longer market & data science context report, including the state of machine learning adoption for credit underwriting and key issues in developing and managing machine learning models with regard to explainability and fairness.

This fourth edition of our FAQs focuses on emerging techniques to explain complex models and builds on prior FAQs that covered the use of AI in financial services and the importance of model transparency and explainability in the context of machine learning credit underwriting models.

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.

This third edition of our FAQs considers the technological, market, and policy implications of using federated machine learning to improve risk identification across anti-financial crime disciplines, including in customer onboarding where it may facilitate more accurate and inclusive customer due diligence.