QUT ePrints

Transaction mining for fraud detection in ERP Systems

Khan, Roheena Q., Corney, Malcolm W., Clark, Andrew J., & Mohay, George M. (2010) Transaction mining for fraud detection in ERP Systems. Industrial Engineering and Management Systems, 9(2), pp. 141-156.

View at publisher (open access)

Abstract

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

491 since deposited on 26 May 2010
126 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 32390
Item Type: Journal Article
Additional URLs:
Keywords: Fraud Detection, Audit Trail Analysis, Anomaly Detection, Security, Enterprise Resource Planning Systems, Role Mining
ISSN: 1598-7248
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Computer System Security (080303)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2010 [please consult the authors]
Deposited On: 27 May 2010 08:34
Last Modified: 22 Oct 2013 14:01

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page