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

TLDR

Operational risk management is essential for organizations, and in the big data era analytical tools for ORM are evolving rapidly. The paper examines recent academic ORM literature from a data analytics perspective, focusing on trends related to natural and man‑made disasters affecting all aspects of life. The authors review ORM studies from respected OM journals, classifying literature by application fields, analytics techniques, and implementation strategies, and emphasize the need for data monitoring and integrating analytical tools into decision making. The study shows that data analytics tools facilitate ORM and proposes a process for implementing data‑driven ORM, outlining future research directions.

Abstract

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.