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

Ping McLemore; Atanas Mihov

doi:10.1093/rcfs/cfag003

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

Ping McLemore¹; Atanas Mihov²

¹ Federal Reserve Bank of Richmond · ² University of Kansas

The Review of Corporate Finance Studies 2026

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.

DOI: 10.1093/rcfs/cfag003
Language: en
Export: BibTeX
Sources: openalex crossref