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What Drives Cyber Losses at U.S. Banks? Potential Statistical Markers

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Abstract

Bank supervisors and regulators are keen to understand and mitigate bank cyber risks. We model average annual loss (AAL) rates from “attritional” cyber-attacks and other cyber events using new, individual bank level data from the CyberCube “analytics platform” combined with standard bank performance measures. We estimate a variety of regression models to robustly identify the systematic drivers of these loss rates. We find that cyber risk AAL loss rates are significantly U-shaped in bank size, contrary to the view these risks are declining in bank size. Bank cyber risk contains a large idiosyncratic component, so apart from bank size, the explanatory power of standard bank performance measures is limited. Controlling for bank size, more profitable and efficient banks have lower cyber related loss rates.

Suggested Citation

  • Seth Dunbar & Kelly Klemme & Anthony Murphy & Joseph I. Suek & Michael Tindall, 2025. "What Drives Cyber Losses at U.S. Banks? Potential Statistical Markers," Working Papers 2520, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddwp:99991
    DOI: 10.24149/wp2520
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    File URL: https://www.dallasfed.org/research/papers/2025/wp2520
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    More about this item

    Keywords

    banks; cyber losses; econometric models;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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