AI and Operational Losses: Evidence from U.S. Bank Holding Companies
Year: 2026
Type: article
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. (JEL D22, G21, J23, J24, O33)
DOI: 10.1093/rcfs/cfag003
Authors: Ping McLemore, Atanas Mihov
Institutions: Federal Reserve Bank of Richmond, University of Kansas
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7130324257