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

Mieke Jans, Nadine Lybaert, Koen Vanhoof

2008

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

  1. ACFE (2006). 2006 ACFE Report to the nation on occupational fraud and abuse. Technical report, Association of Certified Fraud Examiners.
  2. Brockett, P. L., Derrig, R. A., Golden, L. L., Levine, A., and Alpert, M. (2002). Fraud classification using principal component analysis of RIDITs. The Journal of Risk and Insurance, 69(3):341-371.
  3. Cortes, C., Pregibon, D., and Volinsky, C. (2002). Communities of interest. Intelligent Data Analysis, 6:211- 219.
  4. Estévez, P., Held, C., and Perez, C. (2006). Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems with Applications, 31:337-344.
  5. Fanning, K. and Cogger, K. (1998). Neural network detection of management fraud using published financial data. International Journal of Intelligent Systems in Accounting, Finance & Management, 7:21-41.
  6. Hagenaars, J. A. and McCutcheon, A. L. (2002). Applied Latent Class Analysis. Cambridge University Press.
  7. Kaplan, D. (2004). The Sage Handbook of Quantitative Methodology for the Social Sciences. Thousand Oaks: Sage Publications.
  8. Kim, H. and Kwon, W. J. (2006). A multi-line insurance fraud recognition system: a government-led approach in Korea. Risk Management and Insurance Review, 9(2):131-147.
  9. Kirkos, E., Spathis, C., and Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32:995-1003.
  10. Lynch, A. and Gomaa, M. (2003). Understanding the potential impact of information technology on the susceptibility of organizations to fraudulent employee behaviour. International Journal of Accounting Information Systems, 4:295-308.
  11. Magidson, J. and Vermunt, J. K. (2002). Latent class models for clustering: A comparison with k-means. Canadian Journal of Marketing Research.
  12. PwC (2007). Economic crime: people, culture and controls. the 4th biennial global economic crime survey. Technical report, PriceWaterhouse&Coopers.
Download


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