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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8341

Title: Internal Fraud Risk Reduction: Results of a Data Mining Case Study
Authors: JANS, Mieke
LYBAERT, Nadine
VANHOOF, Koen
Issue Date: 2010
Publisher: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Citation: International Journal of Accounting Information Systems, 11(1). p. 17-41
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.
Notes: This paper won the "Best paper award" at the European Conference on Accounting Information Systems 2008, and hence will be published -after some extra adjustments- in the International Journal of Accounting Information Systems.
URI: http://hdl.handle.net/1942/8341
DOI: 10.1016/j.accinf.2009.12.004
ISSN: 1467-0895
Category: A1
Type: Journal Contribution
Validation: vabb, 2011
Appears in Collections: Research publications

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