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How artificial intelligence incidents affect banks and financial services firms? A study of five firms

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  • Durongkadej, Isarin
  • Hu, Wenyao
  • Wang, Heng Emily

Abstract

We investigate the impact of AI incidents on banking and the financial industries. By analyzing five U.S. banks and financial services firms, we find that the average short-term Cumulative Abnormal Returns (CARs) loss of AI incidents is -21.04 % and the negative impact can spread out to the financial industry with a three-day loss of -0.13 %. Compared to firms without AI incidents, banks and financial services firms with AI incidents have higher bankruptcy risk and lower operational cash flows. To our knowledge, this is the first study analyzing the AI incident impact on the performance of banks and financial services firms.

Suggested Citation

  • Durongkadej, Isarin & Hu, Wenyao & Wang, Heng Emily, 2024. "How artificial intelligence incidents affect banks and financial services firms? A study of five firms," Finance Research Letters, Elsevier, vol. 70(C).
  • Handle: RePEc:eee:finlet:v:70:y:2024:i:c:s1544612324013084
    DOI: 10.1016/j.frl.2024.106279
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    References listed on IDEAS

    as
    1. Braun, Declan & Han, Yue & Wang, Heng Emily, 2023. "The application of feed forward neural networks to merger arbitrage: A return-based analysis," Finance Research Letters, Elsevier, vol. 58(PB).
    2. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    3. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    4. Bartlett, Robert & Morse, Adair & Stanton, Richard & Wallace, Nancy, 2022. "Consumer-lending discrimination in the FinTech Era," Journal of Financial Economics, Elsevier, vol. 143(1), pages 30-56.
    5. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    7. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    8. Ellen Tobback & David Martens, 2019. "Retail credit scoring using fine‐grained payment data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1227-1246, October.
    9. Thomas Schneider & Philip E Strahan & Jun Yang, 2023. "Bank Stress Testing: Public Interest or Regulatory Capture?," Review of Finance, European Finance Association, vol. 27(2), pages 423-467.
    10. Williams, Barry, 2016. "The impact of non-interest income on bank risk in Australia," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 16-37.
    11. Moenninghoff, Sebastian C. & Ongena, Steven & Wieandt, Axel, 2015. "The perennial challenge to counter Too-Big-to-Fail in banking: Empirical evidence from the new international regulation dealing with Global Systemically Important Banks," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 221-236.
    12. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    13. Bhagat, Sanjai & Bolton, Brian & Lu, Jun, 2015. "Size, leverage, and risk-taking of financial institutions," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 520-537.
    14. Boehmer, Ekkehart & Grammig, Joachim & Theissen, Erik, 2007. "Estimating the probability of informed trading--does trade misclassification matter?," Journal of Financial Markets, Elsevier, vol. 10(1), pages 26-47, February.
    15. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
    16. Carl R. Chen & Ying Sophie Huang & Ting Zhang, 2017. "Non-interest Income, Trading, and Bank Risk," Journal of Financial Services Research, Springer;Western Finance Association, vol. 51(1), pages 19-53, February.
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    Cited by:

    1. Li, Zhen, 2026. "Does artificial intelligence enhance bank profitability? Evidence from China," Finance Research Letters, Elsevier, vol. 90(C).
    2. Zhang, Yang & Jiang, Manru, 2026. "Digital marketing, social media sentiment, and corporate cash flow," Finance Research Letters, Elsevier, vol. 92(C).
    3. Ma, Yuan & Zhang, Wenchao & Ma, Chengxiang & Ai, Yudong & Hu, Jun, 2025. "Artificial intelligence, data elements, digital economy, and corporate innovation performance," International Review of Economics & Finance, Elsevier, vol. 103(C).
    4. Zhou, Huilin & Wang, Linhui & Cao, Yutong & Li, Jincheng, 2025. "The impact of artificial intelligence on labor market: A study based on bibliometric analysis," Journal of Asian Economics, Elsevier, vol. 98(C).
    5. Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2025. "Do AI incidents and hazards matter for AI-themed cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 74(C).
    6. Deng, Xiaomeng & Qin, Chuan, 2026. "Artificial intelligence and corporate financialization," International Review of Financial Analysis, Elsevier, vol. 110(C).
    7. Shi, Yu & Wu, Tong & Qin, Chuan & Liu, Bailu, 2025. "The value-creating potential of AI: A multi-dimensional analysis of effects and mechanisms," International Review of Financial Analysis, Elsevier, vol. 108(PB).

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    Keywords

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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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