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

Mieke Jans, Nadine Lybaert and Koen Vanhoof

International Journal of Accounting Information Systems, 2010, vol. 11, issue 1, 17-41

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.

Keywords: Internal fraud; Data mining; Risk reduction; Latent class clustering (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:11:y:2010:i:1:p:17-41

DOI: 10.1016/j.accinf.2009.12.004

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