{"title":"Data driven operational risk management","authors":"Sai-Ho Chung, Stein W. Wallace, Xin Wen","doi":"10.1007/s10479-025-06598-5","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"348 2","pages":"777 - 781"},"PeriodicalIF":4.4000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10479-025-06598-5.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Operations Research","FirstCategoryId":"91","ListUrlMain":"https://link.springer.com/article/10.1007/s10479-025-06598-5","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications.
In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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