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Models behaving badly: The limits of data-driven lending

Author

Listed:
  • Itzhak Ben-David
  • Mark J. Johnson
  • René M. Stulz

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.

Suggested Citation

  • Itzhak Ben-David & Mark J. Johnson & René M. Stulz, 2025. "Models behaving badly: The limits of data-driven lending," Review of Finance, European Finance Association, vol. 29(3), pages 711-745.
    • Handle: RePEc:oup:revfin:v:29:y:2025:i:3:p:711-745.
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    File URL: http://hdl.handle.net/10.1093/rof/rfaf009
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    More about this item

    Keywords

    data-driven lending; fintech; model risk; small business lending; COVID-19; credit supply;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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