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Internal fraud risk reduction: Results of a data mining case study

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  • Jans, Mieke
  • Lybaert, Nadine
  • Vanhoof, Koen

Abstract

Corporate fraud represents a huge cost to the current economy. Academic literature has demonstrated how data mining techniques can be of value in the fight against fraud. This research has focused on fraud detection, mostly in a context of external fraud. In this paper, we discuss the use of a data mining approach to reduce the risk of internal fraud. Reducing fraud risk involves both detection and prevention. Accordingly, a descriptive data mining strategy is applied as opposed to the widely used prediction data mining techniques in the literature. The results of using a multivariate latent class clustering algorithm to a case company's procurement data suggest that applying this technique in a descriptive data mining approach is useful in assessing the current risk of internal fraud. The same results could not be obtained by applying a univariate analysis.

Suggested Citation

  • Jans, Mieke & Lybaert, Nadine & Vanhoof, Koen, 2010. "Internal fraud risk reduction: Results of a data mining case study," International Journal of Accounting Information Systems, Elsevier, vol. 11(1), pages 17-41.
    • Handle: RePEc:eee:ijoais:v:11:y:2010:i:1:p:17-41
    DOI: 10.1016/j.accinf.2009.12.004
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    References listed on IDEAS

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    1. Hunsoo Kim & W. Jean Kwon, 2006. "A Multi‐Line Insurance Fraud Recognition System: A Government‐Led Approach in Korea," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 9(2), pages 131-147, September.
    2. Elsas, Ph.I., 2008. "X-raying Segregation of Duties: Support to illuminate an enterprise's immunity to solo-fraud," International Journal of Accounting Information Systems, Elsevier, vol. 9(2), pages 82-93.
    3. Mock, Theodore J. & Sun, Lili & Srivastava, Rajendra P. & Vasarhelyi, Miklos, 2009. "An evidential reasoning approach to Sarbanes-Oxley mandated internal control risk assessment," International Journal of Accounting Information Systems, Elsevier, vol. 10(2), pages 65-78.
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    Citations

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    Cited by:

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    2. Ahmad Faisal Hayek & Nora Azima Noordin & Khaled Hussainey, 2022. "Machine Learning and External Auditor Perception: An Analysis for UAE External Auditors Using Technology Acceptance Model," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(4), pages 475-500, December.
    3. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    4. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    5. Koreff, Jared & Weisner, Martin & Sutton, Steve G., 2021. "Data analytics (ab) use in healthcare fraud audits," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    6. Bradford, Marianne & Earp, Julia B. & Grabski, Severin, 2014. "Centralized end-to-end identity and access management and ERP systems: A multi-case analysis using the Technology Organization Environment framework," International Journal of Accounting Information Systems, Elsevier, vol. 15(2), pages 149-165.
    7. Werner, Michael, 2017. "Financial process mining - Accounting data structure dependent control flow inference," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 57-80.
    8. 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.

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