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Internal Fraud Risk Reduction - Results of a Data Mining Case Study

8 years 10 months ago
Internal Fraud Risk Reduction - Results of a Data Mining Case Study
Corporate fraud these days represents a huge cost to our economy. Academic literature already concentrated on how data mining techniques can be of value in the fight against fraud. All this research focusses 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 comprehends both detection and prevention, and therefore we apply descriptive data mining 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.
Mieke Jans, Nadine Lybaert, Koen Vanhoof
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICEIS
Authors Mieke Jans, Nadine Lybaert, Koen Vanhoof
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