HEC Research Papers Series,
HEC Paris

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.

Keywords: generative artificial intelligence; stress testing; retail credit risk; mortgage lending

JEL-codes: G21; G28; O33

37 pages, First version: April 28, 2026. Revised: May 1, 2026.

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