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AI and Operational Losses: Evidence from U.S. Bank Holding Companies

Ping McLemore & Atanas Mihov

Review of Corporate Finance Studies2026https://doi.org/10.1093/rcfs/cfag003article
AJG 3ABDC A*
Weight
0.50

Abstract

This study demonstrates that banking organizations with higher artificial intelligence (AI) investments are exposed to more operational risk. Using comprehensive supervisory data on operational losses from large U.S. bank holding companies (BHCs) combined with detailed company-level data on AI-skilled human capital, we show that BHCs with more AI investments suffer higher operational losses per dollar of total assets. The impact of AI investments on operational losses significantly varies by loss type and is driven by external fraud, client-related issues, and system failures. These losses stem not only from small, frequent incidents but also from severe, tail-risk events. The risk-enhancing effect of AI is more pronounced for BHCs with weaker risk management practices. Our findings have important implications for banking performance, risk, and supervision.

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https://doi.org/https://doi.org/10.1093/rcfs/cfag003

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@article{ping2026,
  title        = {{AI and Operational Losses: Evidence from U.S. Bank Holding Companies}},
  author       = {Ping McLemore & Atanas Mihov},
  journal      = {Review of Corporate Finance Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/rcfs/cfag003},
}

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AI and Operational Losses: Evidence from U.S. Bank Holding Companies

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.