Keywords
data-driven lending, model miscalibration, small business credit supply
Abstract
Data-driven lending relies on the calibration of models using training periods. We find that this type of lending is not resilient in the presence of economic conditions that are materially different from those experienced during the training period. Using data from a small business fintech lending platform, we document that the small business credit supply collapsed during the COVID-19 crisis of March 2020 even though the demand for loans doubled relative to pre-pandemic levels. As the month progressed, most lenders significantly reduced or halted their lending activities, likely due to the heightened risk of model miscalibration under the new economic conditions.
Original Publication Citation
Models Behaving Badly: The Limits of Data-driven Lending (with Itzhak Ben-David and René Stulz), R&R, Review of Finance
BYU ScholarsArchive Citation
Ben-David, Itzhak; Johnson, Mark J.; and Stulz, René M., "Models Behaving Badly: The Limits of Data-Driven Lending" (2021). Faculty Publications. 9243.
https://scholarsarchive.byu.edu/facpub/9243
Document Type
Working Paper
Publication Date
2021
Publisher
Review of Finance
Language
English
College
Marriott School of Business
Department
Finance
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