Title
Internal fraud risk reduction: results of a data mining case study Internal fraud risk reduction: results of a data mining case study
Author
Faculty/Department
Faculty of Applied Economics
Publication type
article
Publication
Amsterdam ,
Subject
Economics
Source (journal)
International journal of accounting information systems. - Amsterdam
Volume/pages
11(2010) :1 , p. 17-41
ISSN
1467-0895
vabb
c:vabb:305319
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Corporate fraud represents a huge cost to the current economy. Academic literature has demonstrated how data mining techniques can be of value in the fight against fraud. This research has focused 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 involves both detection and prevention. Accordingly, a descriptive data mining strategy is applied 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 sameresults could not be obtained by applying a univariate analysis.
E-info
https://repository.uantwerpen.be/docman/iruaauth/5538d2/70bfb67e407.pdf
Handle