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

Author

Listed:
  • Christophe Hurlin

    (LEO - Laboratoire d'Économie d'Orleans [UMR7322] - UO - Université d'Orléans - UT - Université de Tours - NEOLAiA - NEOLAiA European University = Université Européenne NEOLAÏA - CNRS - Centre National de la Recherche Scientifique)

  • Christophe Pérignon

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

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

  • Christophe Hurlin & Christophe Pérignon, 2026. "The Safe-Tail Paradox: Stress Testing AI Exposure of Banks Borrowers," Working Papers hal-05688563, HAL.
    • Handle: RePEc:hal:wpaper:hal-05688563
    DOI: 10.2139/ssrn.6633858
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