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

Internal Fraud Risk Reduction - Results of a Data Mining Case Study,Mieke Jans,Nadine Lybaert,Koen Vanhoof

Internal Fraud Risk Reduction - Results of a Data Mining Case Study   (Citations: 1)
BibTex | RIS | RefWorks Download
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 flght 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.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
    • ...In another paper by Jans et al. (2008) a data mining approach is proposed as a complement to the internal control framework...
    • ...The suggested framework of that study (and applied in a case study) can be found in Figure 1. We refer to Jans et al. (2008) for a detailed description of this framework...
    • ...In this paper we present an extended framework, based on a previous work of Jans et al. (2008), to apply process mining in the context of internal fraud risk reduction...

    Mieke Janset al. Business Process Mining for Internal Fraud Risk Reduction: Results of ...

Sort by: