Detecting Fraud in Accounting: A Systematic Review of Theories, Models, and Techniques 2000–2025
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Abstract
This study aims to systematically investigate the evolution of theories, models, and techniques in the field of accounting fraud detection from 2000 to 2025. As fraudulent financial reporting continues to undermine the credibility of accounting information and destabilize global economic systems, there is an urgent need to critically evaluate existing detection frameworks and technologies. Employing a qualitative research design based on a systematic literature review methodology, this study analyzed 150 peer-reviewed academic sources across multidisciplinary databases using thematic synthesis. The analysis was guided by a structured protocol that included defined inclusion and exclusion criteria, thematic coding, and data triangulation using NVivo software. The findings reveal a significant theoretical progression from foundational models like the Fraud Triangle to more complex and integrative frameworks, including the Fraud Diamond, the MICE model, and behavioral theories such as the “Dark Triad.” In parallel, empirical evidence highlights the growing dominance of machine learning and hybrid analytical models over traditional statistical techniques in fraud detection. Emerging technologies such as blockchain, big data analytics, and natural language processing are found to be instrumental in enhancing real-time detection capabilities, though challenges remain in interpretability, ethical governance, and data security. The study also identifies critical research gaps, including the need for cross-cultural validation, longitudinal analysis, and interdisciplinary collaboration. These findings contribute to both academic discourse and managerial practice by offering a comprehensive and forward-looking synthesis, serving as a foundational reference for future innovation in accounting fraud prevention.
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References
Abbasi, A., Albrecht, C. C., Vance, A., & Hansen, J. (2018). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 42(2), 529–557. https://doi.org/10.25300/MISQ/2018/14043
Chen, Y., Huang, Z., & Wang, X. (2021). Financial fraud detection using deep learning approaches. Expert Systems with Applications, 181, 115146. https://doi.org/10.1016/j.eswa.2021.115146
Cohen, J., Krishnamoorthy, G., & Wright, A. (2017). Enterprise risk management and the financial reporting process: The experiences of audit committee members, CFOs, and external auditors. Contemporary Accounting Research, 34(2), 1172–1209. https://doi.org/10.1111/1911-3846.12294
DiGabriele, J. A. (2017). The profile of forensic accountants: A multivariate analysis. Journal of Forensic & Investigative Accounting, 9(3), 610–630. https://doi.org/10.2139/ssrn.3052385
Dong, Y., Xu, Y., & Li, L. (2020). Fraud detection on financial statements using machine learning. Computers & Security, 97, 101947. https://doi.org/10.1016/j.cose.2020.101947
Dorminey, J., Fleming, A. S., Kranacher, M. J., & Riley, R. A. (2012). The evolution of fraud theory. Issues in Accounting Education, 27(2), 555–579. https://doi.org/10.2308/iace-50131
Dyck, A., Morse, A., & Zingales, L. (2019). How pervasive is corporate fraud? Review of Accounting Studies, 24(2), 574–595. https://doi.org/10.1007/s11142-018-9470-5
Free, C. (2015). Looking through the fraud triangle: A review and call for new directions. Meditari Accountancy Research, 23(2), 175–196. https://doi.org/10.1108/MEDAR-02-2015-0009
Gong, X., Li, Y., & Wang, Y. (2018). Text mining for financial fraud detection: A systematic review. IEEE Access, 6, 50843–50856. https://doi.org/10.1109/ACCESS.2018.2869722
Huang, W., Lee, J., & Chen, H. (2020). Detecting deceptive discussions in earnings conference calls. Decision Support Systems, 130, 113229. https://doi.org/10.1016/j.dss.2019.113229
Kwon, S., & Kim, H. J. (2020). A review of financial fraud detection studies: Methodologies, models, and features. Journal of Data and Information Quality, 12(4), 1–23. https://doi.org/10.1145/3418002
Lokanan, M. E. (2015). Challenges to the fraud triangle: Questions on its usefulness. Accounting Forum, 39(3), 201–224. https://doi.org/10.1016/j.accfor.2015.01.002
Murphy, P. R., & Free, C. (2016). Broadening the fraud triangle: Instrumental climate and fraud. Behavioral Research in Accounting, 28(1), 41–56. https://doi.org/10.2308/bria-51182
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2017). The application of data mining techniques in financial fraud detection: A classification framework and an academic review. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2017.04.006
Omoteso, K. (2020). Audit quality and fraud detection in financial reporting. Managerial Auditing Journal, 35(7), 903–926. https://doi.org/10.1108/MAJ-06-2019-2343
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
Raza, S. A., Jawaid, S. T., & Shabbir, M. S. (2021). A hybrid model for fraud detection in financial statements using machine learning. Technological Forecasting and Social Change, 166, 120605. https://doi.org/10.1016/j.techfore.2021.120605
Sikka, P. (2015). The hand of accounting and accountancy firms in deepening income and wealth inequalities and the economic crisis: Some evidence. Critical Perspectives on Accounting, 30, 46–62. https://doi.org/10.1016/j.cpa.2013.02.003
Tapscott, D., & Tapscott, A. (2016). Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. Portfolio. https://doi.org/10.2139/ssrn.2744751
Trompeter, G. M., Carpenter, T. D., Desai, N., Jones, K. L., & Riley Jr., R. A. (2014). A synthesis of fraud-related research. Auditing: A Journal of Practice & Theory, 33(2), 287–321. https://doi.org/10.2308/ajpt-10625
Wolfe, D. T., & Hermanson, D. R. (2004). The fraud diamond: Considering the four elements of fraud. The CPA Journal, 74(12), 38–42. https://www.cpajournal.com/2004/12/01/the-fraud-diamond-considering-the-four-elements-of-fraud/
Yermack, D. (2017). Corporate governance and blockchains. Review of Finance, 21(1), 7–31. https://doi.org/10.1093/rof/rfw074
Zhang, Y., Li, S., & Liu, L. (2021). Detecting financial fraud using ensemble learning and feature engineering. Applied Intelligence, 51, 7163–7179. https://doi.org/10.1007/s10489-020-02063-z