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Artificial Intelligence and Risk-Taking in Banking

Author

Listed:
  • Tudor-Andrei Drăgan

    (Babeş-Bolyai University of Cluj-Napoca)

  • Wenjing Jiang

    (Babeş-Bolyai University of Cluj-Napoca)

  • Simona Nistor

    (Babes-Bolyai University - Department of Finance)

  • Steven Ongena

    (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR))

Abstract

We study how banks’ adoption of artificial intelligence (AI) affects risk-taking and stability using data on major listed Chinese banks (2013–2024). We construct an AI-intensity index from agent adoption, cloud computing, and big data measures. Higher AI intensity is associated with greater risk and lower stability: a one–standard deviation increase reduces the Z‑score by 0.25 units (about 22%) next year. This operates mainly via higher earnings volatility and lower profitability, not weaker capitalization. The effect is stronger for banks with low liquidity, unstable funding, and weak loss-absorption, implying AI can amplify risk and requires tighter prudential oversight.

Suggested Citation

  • Tudor-Andrei Drăgan & Wenjing Jiang & Simona Nistor & Steven Ongena, 2026. "Artificial Intelligence and Risk-Taking in Banking," Swiss Finance Institute Research Paper Series 26-25, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp2625
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    More about this item

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • 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
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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