EMERGING FORENSIC ACCOUNTING TECHNOLOGIES FOR FRAUD DETECTION: A COMPREHENSIVE SCOPING REVIEW
DOI:
https://doi.org/10.35631/AIJBES.828049Keywords:
Artificial Intelligence, Data Analytics, Digital Forensics, Emerging Technologies, Forensic Accounting, Fraud Detection, Machine Learning, Scoping ReviewAbstract
As the world becomes digital, financial fraud too is evolving and is a challenge for organisations today to detect and prevent frauds. The fraud schemes get more involved and rely on the latest technology, and traditional audit methods are no longer able to catch up. This scoping review follows the PRISMA-ScR guidelines and the Arksey and O'Malley (2005) framework to analyse how forensic accounting is evolving with the development of new technologies, using a combination of Web of Science and Scopus for literature collection, which results in 116 empirical studies between 2022 and 2024 (89 from Web of Science and 27 from Scopus). Five themes were identified from the analysis. The technology-based fraud detection using artificial intelligence and machine learning was the most notable which was seen in 67 studies (57.8%) with a range of accuracy between 93% and 98.58%. Although the role of technology is becoming increasingly important, there were still 18 studies (15.5%) that emphasized the importance of the auditor's role in supporting the technological tools. Governance and internal controls were found in 21 studies (18.1%) and professional skepticism in 12 studies (10.3%) were organizational and individual factors, respectively. Specifically, the highest accuracy was obtained by using Random Forest (98.58%), K-Nearest Neighbor (95.89%) and Convolutional Neural Networks (95.8%). The hybrid combinations of Multiple Benford's Law models and the K-Means clustering technique were found to be useful for the accuracy of the detection increased from 40% to 93.33%. In terms of geographical distribution, Asian markets are the most prolific and more than 68 studies (58.6%) were published in Asia of which more than 24 (24.1%) were published in China and 12.9% in Indonesia, suggesting the region's high rate of digitalization and dire need for fraud detection. African and Latin American research was very little (4.3% combined) and therefore there was a huge research gap. There were a couple of limitations identified: Most studies (94.8%) were of cross-sectional designs which overlooked longitudinal perspectives and research into implementation challenges is still limited, as is research into the cost benefit analysis and the ethical implications of AI-based fraud detection. This review provides a comprehensive, up-to-date literature review and integrates the latest developments in forensic accounting technology, providing recommendations for organizations, policy makers and researchers.
Downloads
References
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113.
ACFE. (2022). Occupational Fraud 2022: A Report to the Nations. Austin, TX: Association of Certified Fraud Examiners.
Albashrawi, M., & Loraas, T. (2021). Detecting financial fraud using data mining techniques. Journal of Data Science, 9(2), 243-266.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2020). Analytical procedures in external auditing. Journal of Accounting Literature, 45, 188-214.
Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32.
Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.
Bhasin, M. L. (2016). Contribution of forensic accounting to corporate governance. International Business Management, 10(4), 479-492.
Bhasin, M. L. (2016). Survey of skills required by forensic accountants. European Journal of Accounting Research, 4(1), 87-104.
Bierstaker, J. L., Burnaby, P., & Thibodeau, J. (2001). The impact of information technology on audit process. Managerial Auditing Journal, 16(3), 159-164.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of Big Data's impact on audit judgment. Accounting Horizons, 29(2), 451-468.
Cao, M., Hirschi, A., & Faber, A. (2021). Data-driven fraud detection: Machine learning applications. Journal of Financial Crime, 28(2), 524-537.
Casey, E. (2011). Digital Evidence and Computer Crime (3rd ed.). Academic Press.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
COSO. (2013). Internal Control-Integrated Framework. Durham, NC: COSO.
Crumbley, D. L., Heitger, L. E., & Smith, G. S. (2019). Forensic and Investigative Accounting (8th ed.). Chicago: CCH Incorporated.
Dai, J., & Vasarhelyi, M. A. (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 5-21.
Daudt, H. M., van Mossel, C., & Scott, S. J. (2013). Enhancing the scoping study methodology. BMC Medical Research Methodology, 13(1), 48.
Debreceny, R., Gray, G. L., & Rahman, A. (2002). The determinants of Internet financial reporting. Journal of Accounting and Public Policy, 21(4-5), 371-394.
Deloitte. (2023). Global Fraud Survey. London: Deloitte Forensic.
DiGabriele, J. A. (2008). An empirical investigation of forensic accountant skills. Journal of Education for Business, 83(6), 331-338.
Earley, C. E. (2015). Data analytics in auditing. Business Horizons, 58(5), 493-500.
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.
Hurtt, R. K. (2010). Development of a scale to measure professional skepticism. Auditing: A Journal of Practice & Theory, 29(1), 149-171.
IMF. (2021). Financial Technology and Financial Inclusion. Washington, DC: IMF.
IMF. (2021). The Promise of Fintech. Washington, DC: International Monetary Fund.
KPMG. (2022). Global Banking Fraud Survey. London: KPMG International.
Kranacher, M. J., Riley, R., & Wells, J. T. (2010). Forensic Accounting and Fraud Examination. Hoboken, NJ: John Wiley & Sons.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lenz, R., & Hahn, U. (2015). A synthesis of empirical internal control research. Journal of Accounting Literature, 35, 96-116.
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5(1), 69.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms. Big Data & Society, 3(2), 1-21.
Mongeon, P., & Paul-Hus, A. (2016). Journal coverage of Web of Science and Scopus. Scientometrics, 106(1), 213-228.
Munn, Z., Peters, M. D., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? BMC Medical Research Methodology, 18(1), 143.
Near, J. P., & Miceli, M. P. (1995). Effective whistleblowing. Academy of Management Review, 20(3), 679-708.
Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). Application of data mining in financial fraud detection. Decision Support Systems, 50(3), 559-569.
Nigrini, M. J. (2012). Benford's Law: Applications for Forensic Accounting. Hoboken, NJ: John Wiley & Sons.
Nikkel, B. (2020). Fintech forensics: Criminal investigation in financial technologies. Forensic Science International: Digital Investigation, 33, 200908.
O'Neil, C. (2016). Weapons of Math Destruction. New York: Crown.
Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., et al. (2020). Updated methodological guidance for scoping reviews. JBI Evidence Synthesis, 18(10), 2119-2126.
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). Comprehensive survey of data mining-based fraud detection. arXiv:1009.6119.
Popova, V. (2013). Exploration of skepticism and audit judgments. Managerial Auditing Journal, 28(2), 140-160.
PwC. (2022). Global Economic Crime and Fraud Survey. London: PricewaterhouseCoopers.
Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2006). Publication Bias in Meta-Analysis. Chichester: John Wiley & Sons.
Schmitz, J., & Leoni, G. (2019). Accounting and auditing at the time of blockchain. Australian Accounting Review, 29(2), 331-342.
Singleton, T. W., & Singleton, A. J. (2010). Fraud Auditing and Forensic Accounting (4th ed.). Hoboken, NJ: John Wiley & Sons.
Sutton, S. G., Holt, M., & Arnold, V. (2016). Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60-73.
Thomas, J., & Harden, A. (2008). Methods for thematic synthesis. BMC Medical Research Methodology, 8(1), 45.
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., et al. (2018). PRISMA extension for scoping reviews. Annals of Internal Medicine, 169(7), 467-473.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology. MIS Quarterly, 36(1), 157-178.
West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection. Computers & Security, 57, 47-66.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology. Journal of Enterprise Information Management, 28(3), 443-488.
World Bank. (2022). Digital Financial Services in Developing Economies. Washington, DC: World Bank Group.
Zhou, Z. H. (2012). Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: Chapman and Hall/CRC.
