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  4. Role of Analytics for Operational Risk Management in the Era of Big Data
 
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Role of Analytics for Operational Risk Management in the Era of Big Data

Journal
Decision Sciences
Journal Volume
51
Journal Issue
6
Pages
1320-1346
Date Issued
2020
Author(s)
Araz O.M.
TSAN MING CHOI  
Olson D.L.
Salman F.S.
DOI
10.1111/deci.12451
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084152162&doi=10.1111%2fdeci.12451&partnerID=40&md5=264d4d34981a7efbd5a58e220577a095
https://scholars.lib.ntu.edu.tw/handle/123456789/612169
Abstract
Operational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man-made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well-regarded OM journals, including both empirical and analytical outlets. We highlight how the use of data analytics tools and methods have facilitated ORM. We discuss the need for data monitoring and the integration of various analytical tools into decision making processes by classifying the literature on application fields, analytics techniques, and the strategies used for implementation. We summarize our findings and propose a process to implement data-driven ORM with future research directions. ? 2020 Decision Sciences Institute
Subjects
Analytics; Big Data; Operations; Risk; Supply Chains
Publisher
Blackwell Publishing Ltd
Type
conference paper

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