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

Abstract

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.