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 tech-niques 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 detec-tion 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 apply-ing 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.