INTERNAL FRAUD RISK REDUCTION - Results of a Data Mining Case Study

Mieke Jans, Nadine Lybaert, Koen Vanhoof

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.

References

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Paper Citation


in Harvard Style

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 2: ICEIS, ISBN 978-989-8111-37-1, pages 161-166. DOI: 10.5220/0001679201610166


in Bibtex Style

@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 2: ICEIS,},
year={2008},
pages={161-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001679201610166},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - INTERNAL FRAUD RISK REDUCTION - Results of a Data Mining Case Study
SN - 978-989-8111-37-1
AU - Jans M.
AU - Lybaert N.
AU - Vanhoof K.
PY - 2008
SP - 161
EP - 166
DO - 10.5220/0001679201610166