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Authors: Mieke Jans 1 ; Nadine Lybaert 1 and Koen Vanhoof 2

Affiliations: 1 KIZOK Research Institute, Hasselt University, Belgium ; 2 Research Group Data Analysis and Modeling, Hasselt University, Belgium

Keyword(s): Internal Fraud, Data Mining, Risk Reduction.

Related Ontology Subjects/Areas/Topics: Applications of Expert Systems ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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.

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Paper citation in several formats:
Jans, M.; Lybaert, N. and Vanhoof, K. (2008). INTERNAL FRAUD RISK REDUCTION - Results of a Data Mining Case Study. In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS; ISBN 978-989-8111-37-1; ISSN 2184-4992, SciTePress, pages 161-166. DOI: 10.5220/0001679201610166

@conference{iceis08,
author={Mieke Jans. and Nadine Lybaert. and Koen Vanhoof.},
title={INTERNAL FRAUD RISK REDUCTION - Results of a Data Mining Case Study},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS},
year={2008},
pages={161-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001679201610166},
isbn={978-989-8111-37-1},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS
TI - INTERNAL FRAUD RISK REDUCTION - Results of a Data Mining Case Study
SN - 978-989-8111-37-1
IS - 2184-4992
AU - Jans, M.
AU - Lybaert, N.
AU - Vanhoof, K.
PY - 2008
SP - 161
EP - 166
DO - 10.5220/0001679201610166
PB - SciTePress

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