Detecting and preventing fraud with big data analytics: Auditing perspective

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Abstract

Fraud exposes a business to a variety of significant financial risks that can threaten both its profitability and public image. All firms are almost certain to be victimized by some form of economic crime or fraud. As a result, the business world’s revolution in big data and data analytics plays a critical role in the establishment of competitive companies, as big data is already being used in a wide variety of industries (Rezaee & Wang, 2019) and is referred to as the next frontier in terms of productivity, innovation, and competition (Al-Marzooqi, 2021). This paper aims to explore how auditors use big data analytics to detect and prevent fraud in their audit work, the benefits, and barriers of incorporating big data analytics into audit practice. Methodologically, this study conducted a library search and evaluated prior literature reviews on the subject of big data analytics and the auditing profession. The resources span a range of items, from online and print sources to articles in journals and chapters in books. Numerous databases, including Scopus, Web of Science, Science Direct, and Google Scholar, were searched between 2011 and 2022 to compile literature on the subject. This paper makes recommendations on how to improve data analytics approaches for detecting and preventing fraud as well as discusses limitations and future studies.

Keywords: Big Data, Data Analytics, Fraud Prevention, Fraud Detection, Auditing

Authors’ individual contribution: Conceptualization — I.R. and R.J.J.; Methodology — N.A.M.H.; Resources — N.A.M.H., S.A.H.S.H., and A.M.M.; Formal Analysis — R.J.J. and N.A.M.H.; Writing — Original Draft — I.R. and N.A.M.H.; Writing — Review & Editing — I.R. and R.J.J.; Supervision — I.R.; Project Administration — N.A.M.H., S.A.H.S.H., and A.M.M.

Declaration of conflicting interests: The Authors declare that there is no conflict of interest.

Acknowledgements: The Authors gratefully acknowledge Universitas Swadaya Gunung Jati (Indonesia), Universiti Teknologi MARA (Malaysia), Esco Micro (M) Sdn Bhd (Malaysia), National Audit Department of Malaysia, and Universitas Negeri Jakarta (Indonesia) for all supports and resources.

JEL Classification: M4

Received: 06.01.2022
Accepted: 25.08.2022
Published online: 26.08.2022

How to cite this paper: Rosnidah, I., Johari, R. J., Mohd Hairudin, N. A., Hussin, S. A. H. S., & Musyaffi, A. M. (2022). Detecting and preventing fraud with big data analytics: Auditing perspective. Journal of Governance & Regulation, 11(4), 8–15. https://doi.org/10.22495/jgrv11i4art1