This empirical white paper assesses the capabilities and limitations of available model diagnostic tools in helping lenders manage machine learning underwriting models. It focuses on the tools’ production of information relevant to adverse action, fair lending, and model risk management requirements.

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 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 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.

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.

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.

FinRegLab partnered with the Financial Health Network, Flourish Ventures, and Mitchell Sandler to provide a working paper summarizing the current U.S. federal legal framework governing consumer financial data with the goal of laying a foundation for future policy analyses and discussions.