This policy analysis explores the regulatory and public policy implications of the increasing use of machine learning models and explainability and fairness techniques for credit underwriting in depth, particularly for model risk management, consumer disclosures, and fair lending compliance.

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 report provides a detailed snapshot of the use of cash-flow data in U.S. consumer lending and the development of the system for transferring data between firms, in addition to analyzing policy and regulatory issues raised by cash-flow underwriting in both consumer and small business credit markets.

This overview highlights key themes from our longer market context and policy analysis, including outlining options for action by industry, regulators, and Congress.