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Data driven operational risk management

Author

Listed:
  • Sai-Ho Chung

    (The Hong Kong Polytechnic University)

  • Stein W. Wallace

    (NHH Norwegian School of Economics)

  • Xin Wen

    (The Hong Kong Polytechnic University)

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.

Suggested Citation

  • Sai-Ho Chung & Stein W. Wallace & Xin Wen, 2025. "Data driven operational risk management," Annals of Operations Research, Springer, vol. 348(2), pages 777-781, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:2:d:10.1007_s10479-025-06598-5
    DOI: 10.1007/s10479-025-06598-5
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    References listed on IDEAS

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