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Rise of the Machines: The Impact of Automated Underwriting


  • Jansen, Mark

    (U of Utah)

  • Nguyen, Hieu

    (U of Utah)

  • Shams, Amin

    (Ohio State U)


Using a randomized experiment in auto lending, we provide evidence of higher loan profitability with algorithmic machine underwriting relative to human underwriting. Machine-underwritten loans generate 10.2% higher loan-level profit than human-underwritten loans in a sample of 140,000 randomly assigned applications. The loans underwritten by machines not only have higher interest rates but also realize a 6.8% lower incidence of default. The performance gap is mainly driven by loans with higher complexity and where potential for agency conflicts is the highest. These results are consistent with algorithmic underwriting mitigating agency conflicts and humans' limited capacity for analyzing complex problems.

Suggested Citation

  • Jansen, Mark & Nguyen, Hieu & Shams, Amin, 2020. "Rise of the Machines: The Impact of Automated Underwriting," Working Paper Series 2020-19, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  • Handle: RePEc:ecl:ohidic:2020-19

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    Cited by:

    1. Tobias Berg & Andreas Fuster & Manju Puri, 2022. "FinTech Lending," Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 187-207, November.
    2. Christophe Hurlin & Christophe Perignon & S├ębastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.

    More about this item

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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