Explainability and Fairness in Machine Learning for Credit Underwriting


Lending decisions informed by credit underwriting models affect the lives of hundreds of millions of Americans. The emerging use of machine learning models has the potential to increase credit access by more accurately identifying applicants who are likely to repay loans and to reduce the number of people given loans that they are unlikely to repay. However, the “black box” nature of many machine learning models has focused attention on model transparency as a critical threshold question for both lenders and regulators.

FinRegLab joined forces with researchers from the Stanford Graduate School of Business to evaluate the capabilities, limitations, and performance of proprietary and open-source tools designed to help lenders address these transparency challenges.

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FinRegLab is an independent, nonprofit organization that conducts research and experiments with new technologies and data to drive the financial sector toward a responsible and inclusive marketplace. The organization also facilitates discourse across the financial ecosystem to inform public policy and market practices. To receive periodic updates on the latest research, subscribe to FRL’s newsletter and visit Follow FinRegLab on LinkedIn and Twitter (X). | 1701 K Street Northwest, Suite 1150, Washington, DC 20006