Headwind or tailwind? How banks interpret borrowers’ AI exposure

Bruno Buchetti, Luca X. Lin, Ixart Miquel-Flores and Christian Eufinger

No 3302, Working Paper Series from European Central Bank

Abstract: General-purpose technologies create large opportunities but make their distribution across firms difficult to assess in real time. We use banks as interpreters of the rise of generative AI by studying lender-specific probability-of-default (PD) estimates for private firms in four large euro-area economies. As public attention to AI rises after ChatGPT, banks lower assessed default risk for borrowers in highly AI-exposed industries by about 5%. The reassessment is weaker for labor-intensive firms and stronger among lenders with pre-existing industry expertise. Yet the favorable industry-level effect is accompanied by a systematic reordering within product markets: banks reassess firms with stronger pre-ChatGPT market positions less favorably relative to their competitors, compressing the risk advantage associated with market leadership. Credit allocation moves in the same direction, with market leaders experiencing relatively lower drawn and committed credit, more relationship exits, and fewer new lenders. The reordering also predicts subsequent bad-credit events beyond banks’ consensus PDs. Internal bank assessments therefore provide a forward-looking measure of how generative AI is expected to reshape competitive risk. JEL Classification: D82, D83, G21, G32, O33

Keywords: artificial intelligence; bank lending; credit risk; generative AI; industry specialization; probability of default; relationship lending; technological change (search for similar items in EconPapers)
Date: 2026-10
Note: 2642726
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