Innovative approaches in forensic accounting: The role of data analytics

Amra Kapo; Lejla Turulja; Zorana Vidačak

doi:10.2478/jfap-2024-0001

Abstract

The integration of data analytics into forensic accounting has revolutionized the detection and prevention of financial fraud. This paper conducts a comprehensive analysis of recent advancements in this field, highlighting the application of machine learning, data mining, and big data techniques in identifying fraudulent activities. By reviewing the latest research and examining case studies, we demonstrate the enhanced accuracy and efficiency these technologies offer over traditional methods. The findings suggest that financial institutions should adopt these advanced tools to mitigate fraud risks and improve overall financial security.

The paper also explores future research directions, emphasizing the need for developing hybrid models and real-time detection systems to further enhance fraud detection capabilities.

References

Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly: Management Information Systems, 36(4). https://doi.org/10.2307/41703508
Search in Google Scholar Back to article
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, pp. 90–113. https://doi.org/10.1016/j.jnca.2016.04.007
Search in Google Scholar Back to article
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), pp. 602–613. https://doi.org/10.1016/j.dss.2010.08.008
Search in Google Scholar Back to article
Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), pp. 235–255. https://doi.org/10.1214/ss/1042727940
Search in Google Scholar Back to article
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). SCARFF: A scalable framework for streaming credit card fraud detection with spark. nformation Fusion, 41, pp. 182–194. https://doi.org/10.1016/j.inffus.2017.09.005
Search in Google Scholar Back to article
Davies, A. (2023). Four Trends Shaping the State of Financial Crime in 2023. Retrieved from: https://complyadvantage.com/insights/four-trends-shaping-the-state-of-financial-crime-in-2023/
Search in Google Scholar Back to article
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), pp. 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Search in Google Scholar Back to article
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, pp. 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
Search in Google Scholar Back to article
Glancy, F. H., & Yadav, S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50(3), pp. 595–601. https://doi.org/10.1016/j.dss.2010.08.010
Search in Google Scholar Back to article
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Search in Google Scholar Back to article
Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2012). Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50(2), pp. 349–392. https://doi.org/10.1111/j.1475-679X.2011.00433.x
Search in Google Scholar Back to article
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), pp. 995–1003. https://doi.org/10.1016/j.eswa.2006.02.016
Search in Google Scholar Back to article
Kroll. (2023). 2023 Fraud and Financial Crime Report. Retrieved from: https://www.kroll.com/en/insights/publications/fraud-and-financial-crime-report
Search in Google Scholar Back to article
Ngai, E. W. T., 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 the literature. Decision Support Systems, 50(3), pp. 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Search in Google Scholar Back to article
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing, 30(2), pp. 19–50. https://doi.org/10.2308/ajpt-50009
Search in Google Scholar Back to article
Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50(2), pp. 491–500. https://doi.org/10.1016/j.dss.2010.11.006
Search in Google Scholar Back to article
Zhou, H., Sun, G., Fu, S., Wang, L., Hu, J., & Gao, Y. (2021). Internet financial fraud detection based on a distributed Big Data approach with Node2vec. IEEE Access, 9, pp. 43378–43386. https://doi.org/10.1109/ACCESS.2021.3062467
Search in Google Scholar Back to article
Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), pp. 570–575. https://doi.org/10.1016/j.dss.2010.08.007
Search in Google Scholar Back to article

Innovative approaches in forensic accounting: The role of data analytics

Abstract

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