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Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

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  • Sonika Gupta
  • Sushil Kumar Mehta

Abstract

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

Suggested Citation

  • Sonika Gupta & Sushil Kumar Mehta, 2024. "Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies," Global Business Review, International Management Institute, vol. 25(5), pages 1290-1313, October.
    • Handle: RePEc:sae:globus:v:25:y:2024:i:5:p:1290-1313
    DOI: 10.1177/0972150920984857
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    References listed on IDEAS

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    1. Emie Famieza Zainudin & Hafiza Aishah Hashim, 2016. "Detecting fraudulent financial reporting using financial ratio," Journal of Financial Reporting and Accounting, Emerald Group Publishing Limited, vol. 14(2), pages 266-278, October.
    2. Jerry W. Lin & Mark I. Hwang & Jack D. Becker, 2003. "A fuzzy neural network for assessing the risk of fraudulent financial reporting," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 18(8), pages 657-665, November.
    3. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    4. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    5. Kathleen A. Kaminski & T. Sterling Wetzel & Liming Guan, 2004. "Can financial ratios detect fraudulent financial reporting?," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 19(1), pages 15-28, January.
    6. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    7. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    8. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    9. Charalambos T. Spathis, 2002. "Detecting false financial statements using published data: some evidence from Greece," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 17(4), pages 179-191, June.
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