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Loan Officer Incentives, Internal Rating Models, and Default Rates

Author

Listed:
  • Tobias Berg
  • Manju Puri
  • Jörg Rocholl

Abstract

Manipulation of hard information has been at the center of a wave of investigations into fraudulent bank behavior, such as mis-selling of mortgages and rigging of London Interbank Offered Rate and Foreign Exchange rates. Despite these prominent cases, little is known as to why employees manipulate hard information. Using almost a quarter million retail loan applications, we show that loan officers who face volume-based incentives significantly manipulate ratings even in settings where ratings are computed using hard information only. Manipulation is widespread across loan officers, with low-performing loan officers manipulating more toward the end of the year. These incentives have a first-order effect on bank profitability, reducing return on equity by 1.5 percentage points. We conclude that reliance on hard information does not overcome loan officer agency problems, and it is important for banks and regulators to take manipulation of hard information into account when using hard information for risk assessment and regulation.

Suggested Citation

  • Tobias Berg & Manju Puri & Jörg Rocholl, 2020. "Loan Officer Incentives, Internal Rating Models, and Default Rates," Review of Finance, European Finance Association, vol. 24(3), pages 529-578.
    • Handle: RePEc:oup:revfin:v:24:y:2020:i:3:p:529-578.
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    File URL: http://hdl.handle.net/10.1093/rof/rfz018
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    Citations

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    Cited by:

    1. Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    2. Bertrand, Jérémie & Burietz, Aurore, 2023. "(Loan) price and (loan officer) prejudice," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 26-42.
    3. Shang, Longfei & Saffar, Walid, 2023. "Employment Protection and Household Mortgage Debt," Journal of Banking & Finance, Elsevier, vol. 149(C).
    4. Ferrando, Annalisa & Popov, Alexander & Udell, Gregory F., 2022. "Unconventional monetary policy, funding expectations, and firm decisions," European Economic Review, Elsevier, vol. 149(C).
    5. Richard Brody & Matias Sokolowski & Reilly White, 2021. "The Potential for Biases in Resolving Loan Problems," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 10(3), pages 57-66, July.
    6. Igor Makarov & Antoinette Schoar, 2022. "Cryptocurrencies and Decentralised Finance," BIS Working Papers 1061, Bank for International Settlements.
    7. Allen N. Berger & Christa H. S. Bouwman & Lars Norden & Raluca A. Roman & Gregory F. Udell & Teng Wang, 2021. "Piercing Through Opacity: Relationships and Credit Card Lending to Consumers and Small Businesses During Normal Times and the COVID-19 Crisis," Working Papers 21-19, Federal Reserve Bank of Philadelphia.
    8. Pierri, Nicola & Timmer, Yannick, 2022. "The importance of technology in banking during a crisis," Journal of Monetary Economics, Elsevier, vol. 128(C), pages 88-104.
    9. Thomas Conlon & Xing Huan & Steven Ongena, 2020. "Operational Risk Capital," Swiss Finance Institute Research Paper Series 20-55, Swiss Finance Institute.
    10. Yuan, Hongqi & Zhou, Yiyuan & Zou, Hong, 2022. "Serving multiple ‘masters’: Evidence from the loan decisions of a publicly listed state-owned bank around a massive economic stimulus programme11The authors can be contacted via yuanhq@fudan.edu.cn, y," Journal of Corporate Finance, Elsevier, vol. 72(C).

    More about this item

    Keywords

    Loan officer incentives; Internal ratings; Hard information;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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