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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8305

Title: Internal Fraud Risk Reduction: Results of a Data Mining Case Study
Authors: JANS, Mieke
LYBAERT, Nadine
VANHOOF, Koen
Issue Date: 2008
Citation: ICEIS 2008: PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS. p. 161-166.
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 technique to reduce the risk of internal fraud. Reducing fraud risk comprehends both detection and prevention, and therefore we apply a descriptive data mining technique as opposed to the widely used prediction data mining techniques in the literature. The results of using a latent class clustering algorithm to a case company’s procurement data suggest that applying this technique of descriptive data mining is useful in assessing the current risk of internal fraud.
URI: http://hdl.handle.net/1942/8305
ISI #: 000259488000025
ISBN: 978-989-811-37-1
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2009
Appears in Collections: Research publications

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