INVESTIGATING FINANCIAL FRAUDS IN THE MODERN LANDSCAPE: A FORENSIC ACCOUNTING PERSPECTIVE IN THE COVID-19 ERA

Miloš Pavlović, Tadija Đukić, Čedomir Gligorić

DOI Number
https://doi.org/10.22190/FUEO230829020P
First page
315
Last page
322

Abstract


This paper explores the evolving landscape of forensic accounting, particularly in the context of changing financial frauds and the challenges posed by the COVID-19 pandemic. The progression of financial fraud over time, driven by technological advancements and shifting business environments, highlights the necessity for adaptive and innovative approaches to fraud detection and prevention. Advanced methodologies, including data analytics, digital forensics, and artificial intelligence, have empowered forensic accountants to confront increasingly sophisticated fraud schemes. The examination of frauds arising from the pandemic underscores the resilience of fraudsters in exploiting vulnerabilities during crises. This paper underscores the importance of forensic accounting in upholding financial integrity, ethical standards, and business resilience. It calls for continuous research, innovation, and vigilance in the field of forensic accounting to counter emerging fraud schemes and evolving business landscapes.


Keywords

Forensic Accounting, Financial Frauds, COVID-19, Fraud Detection, Prevention, Ethical Standards.

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References


Andrei, D. (2021). The impact of the Covid-19 pandemic on Romania's business environment (MPRA Paper No. 109944). University Library of Munich, Germany. Retrieved from: https://mpra.ub.uni-muenchen.de/109944/

Bernard, P., De Freitas, E. M. N., & Maillet, B. B. (2022). A financial fraud detection indicator for investors: an IDeA. Annals of Operations Research, 313, 809-832. https://doi.org/10.1007/s10479-019-03360-6

Brown, N. C., Crowely, R. M., & Elliott, W. B. (2020). What are You Saying? Using Topic to Detect Financial Misreporting. Journal of Accounting Research, 58(1), 237-291. https://doi.org/10.1111/1475-679X.12294

Cao, L. (2020, July 10). AI in Finance: A Review. Retrieved from SSRN: https://ssrn.com/abstract=3647625 or http://dx.doi.org/10.2139/ssrn.3647625

Dake, D. K. (2023, March). Online Recruitment Fraud Detection: A Machine Learning-based Model for Ghanaian Job Websites. International Journal of Computer Applications, 184(51), 20-28. https://doi.org/10.5120/ijca2023922639

Deniswara, K., Mulyawan, A. N., Kesuma, J. T., & Martancti, B. S. (2022). Reimagining a New Transformation of Digital Forensic Accounting: Strategic Analysis of the Use of Big Data Analytics in the Covid-19 Pandemic Era as an Opportunity for the Industries in Indonesia. In: DSDE '22: 2022 the 5th International Conference on Data Storage and Data Engineering. February 2022 (pp.44-49). https://doi.org/10.1145/3528114.3528122

Dohrer, B., & Mayes, C. (2020). 4 key COVID-19 audit risks for 2020 year ends. Journal of Accountancy, June 5, 2020. Available at: https://www.journalofaccountancy.com/news/2020/jun/key-coronavirus-audit-risks-for-2020-year-ends.html

Hossain, M. Z. (2023, May 16). Emerging Trends in Forensic Accounting: Data Analytics, Cyber Forensic Accounting, Cryptocurrencies, and Blockchain Technology for Fraud Investigation and Prevention. Retrieved from SSRN: https://ssrn.com/abstract=4450488 or http://dx.doi.org/10.2139/ssrn.4450488

Jain, D. E., & Lamba, J. (2020). Forensic Accounting: A way to fight, deter and detect fraud. IARS’ International Research Journal, 10(1). https://doi.org/10.51611/iars.irj.v10i1.2020.106

Karpoff, J. M. (2021). The future of financial fraud. Journal of Corporate Finance, 66, 101694. https://doi.org/10.1016/j.jcorpfin.2020.101694

Ngai, E., Hu, Y., Wong, Y. H, Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569. https://doi.org/10.1016/j.dss.2010.08.006

Okereafor, K. (2021). Cybersecurity in the COVID-19 Pandemic (1st ed.). CRC Press. https://doi.org/10.1201/9781003104124

Peecher, M. E., Schwartz, R., & Solomon, I. (2007). It’s all about audit quality: Perspectives on strategic-systems auditing. Accounting, Organizations and Society, 32(4-5), 463-485. https://doi.org/10.1016/j.aos.2006.09.001

Purda, L., & Skillicorn, D. (2015). Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection. Contemporary Accounting Research 32(3), 1193-223. https://doi.org/10.1111/1911-3846.12089

Sun, G., Li, T., Ai, Y., & Li, Q. (2023). Digital finance and corporate financial fraud. International Review of Financial Analysis, 87, 102566. https://doi.org/10.1016/j.irfa.2023.102566

Wang, G., Ma, J., & Chen, G. (2023). Attentive statement fraud detection: Distinguishing multimodal financial data with finegrained attention. Decision Support Systems, 167, 113913. https://doi.org/10.1016/j.dss.2022.113913

Wells, J. T. (2014). Principles of fraud examination. John Wiley & Sons.

Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision support systems, 50(3), 570575. https://doi.org/10.1016/j.dss.2010.08.007




DOI: https://doi.org/10.22190/FUEO230829020P

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