This research brief highlights the need for consumer engagement and administrative flexibility to help distressed borrowers transition smoothly into longer-term repayment plans as pandemic deferral programs end and new programs are implemented.

FinRegLab research finds that tools for managing explainability and fairness in machine learning underwriting models hold promise and that regulatory guidance could encourage more consistent, responsible use.

This paper summarizes the machine learning project’s key empirical research findings and discusses the regulatory and public policy implications to be considered with the increasing use of machine learning models and explainability and fairness techniques.

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 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.