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...

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Bibliographic Details
Published in:Frontiers in artificial intelligence 2021-06, Vol.4, p.695301-695301
Main Authors: Hall, Patrick, Cox, Benjamin, Dickerson, Steven, Ravi Kannan, Arjun, Kulkarni, Raghu, Schmidt, Nicholas
Format: Article
Language:English
Subjects:
Artificial Intelligence
credit underwriting
deep learning—artificial neural network (DL-ANN)
evolutionary learning
fairness
interpretability
XAI (explainable artificial intelligence)
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Summary: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.
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2021.695301