This working paper focuses on consumers who struggled to manage credit card and other unsecured debt during the pandemic, analyzing the extent to which consumers obtained card and other forbearances from lenders and shifts in patterns of consumers who sought credit counseling and enrolled in debt management plans through September 2021.

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.

We conducted an independent analysis of data from six firms that use cash-flow metrics to assess consumer and small business applicants. Using loan level performance data, we evaluated the information’s implications for predictiveness, inclusion, and fairness.