Data driven operational risk management

Sai-Ho Chung (), Stein W. Wallace () and Xin Wen ()
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Sai-Ho Chung: The Hong Kong Polytechnic University
Stein W. Wallace: NHH Norwegian School of Economics
Xin Wen: The Hong Kong Polytechnic University

Annals of Operations Research, 2025, vol. 348, issue 2, No 1, 777-781

Abstract: Abstract Operational risks exist everywhere. With fast changes in the real world, traditional risk management measures become insufficient. Instead, the importance of data-driven approaches increases dramatically. In this special issue, we collect high quality papers on different aspects of operational risk management with data analytics. Both theoretical issues and application results are included. The publications collected cover a wide range of research topics, like the value of blockchains towards risk management in high-tech manufacturing, the convex risk measures for solving risk-averse multistage stochastic programs, the balanced weighted extreme learning machine method for imbalance learning of credit default risk and manufacturing productivity, etc. The insights generated from this special issue can provide crucial guidelines for both the academia and the industry regarding risk management with the support of data analytics.

Date: 2025
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DOI: 10.1007/s10479-025-06598-5

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