A United States Fair Lending Perspective on Machine Learning

The use of machine learning (ML) has become more widespread in many areas of consumer financial services, including credit underwriting and pricing of loans. ML’s ability to automatically learn nonlinearities and interactions in training data is perceived to facilitate faster and more accurate credit decisions, and ML is now a viable challenger to traditional credit modeling methodologies. In this mini review, we further the discussion of ML in consumer finance by proposing uniform definitions of key ML and legal concepts related to discrimination and interpretability. We use the United States legal and regulatory environment as a foundation to add critical context to the broader discussion of relevant, substantial, and novel ML methodologies in credit underwriting, and we review numerous strategies to mitigate the many potential adverse implications of ML in consumer finance.

Fair Lending for Cannabis Banking Justice

This Comment offers a fair lending solution to promote racial equity in cannabis banking reform: amend the Equal Credit Opportunity Act to ensure individuals previously arrested, charged, or convicted for selling, cultivating, or possessing marijuana will not therefore be precluded from loans to start legal cannabis businesses. Given disparities in the criminal enforcement of marijuana laws, this amendment would provide racial justice benefits, while also encouraging entrepreneurship. As a market-based social justice effort, this amendment offers a bipartisan approach to one of the most vexing and contentious issues in marijuana banking reform. Part II of this Comment briefly surveys the federal statutes that have led to an under-banked cannabis industry and discusses the costs of cash for marijuana businesses. It then examines prior reforms proposed by academics, executive-branch officials, and legislators. Part III explores the racial equity concerns that these proposals fail to address, while Part IV offers a fair lending approach for justice in marijuana banking reform.

Should Fair-lending Investigators Better Mix Qualitative and Quantitative Methods?

The author examines a controversy over the methods used by federal agencies to enforce fair-lending laws. Some hold that investigators should use primarily qualitative reviews of documents in loan files in their investigations. Others hold that the qualitative methodology of file reviews is inadequate and needs to be supplemented or replaced by quantitative statistical methods. The author argues that the methods in the federal Interagency Fair Lending Examination Procedure should be revised to use a combination of qualitative and quantitative methods in a better way so that they complement each other. Investigations should rely primarily on quantitative methods to investigate institutional discrimination, but use the qualitative methods of file reviews in the investigation of individual agents for their discriminatory decisions.