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The Safe-Tail Paradox: Stress Testing AI Exposure of Banks Borrowers

Author

Listed:
  • Hurlin, Christophe

    (University of Orleans)

  • Pérignon, Christophe

    (HEC Paris - Finance Department)

Abstract

We document a Safe-Tail Paradox in banks’ credit portfolios: retail borrowers classified as safest by scoring models are also the most exposed to artificial intelligence (AI)-related labor income risk. The paradox arises because AI exposure is positively correlated with borrower characteristics historically associated with low default risk (e.g., stable employment, high income), while AI exposure can weaken repayment capacity through displacement and wage compression. Credit risk therefore becomes concentrated in the safest segments of mortgage portfolios, precisely where regulatory capital buffers are thinnest. We design a borrower-level AI stress test and apply it to a synthetic portfolio calibrated to the French residential mortgage market. As AI adoption intensifies, capital requirements rise sixfold more in the safest class than in the riskiest, highlighting the need for AI-aware risk management.

Suggested Citation

  • Hurlin, Christophe & Pérignon, Christophe, 2026. "The Safe-Tail Paradox: Stress Testing AI Exposure of Banks Borrowers," HEC Research Papers Series 1630, HEC Paris, revised 01 May 2026.
    • Handle: RePEc:ebg:heccah:1630
    DOI: 10.2139/ssrn.6633858
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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • 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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