Show simple item record

dc.contributor.convenorMiklos A. Vasarhelyi
dc.contributor.authorSingh, Kishore
dc.contributor.authorBest, Peter
dc.contributor.authorMula, Joseph
dc.contributor.editorMiklos Vasarhelyi
dc.date.accessioned2017-05-03T16:14:25Z
dc.date.available2017-05-03T16:14:25Z
dc.date.issued2013
dc.date.modified2014-06-27T01:18:57Z
dc.identifier.urihttp://hdl.handle.net/10072/61001
dc.description.abstractFraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii) visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i) a model for proactive fraud detection, ii) methods for visualising user activities in transaction data, iii) a stand-alone MCL-based prototype.
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisher29WCARS
dc.publisher.urihttp://29wcars.org/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename29 World Continuous Auditing and Reporting Symposium
dc.relation.ispartofconferencetitle29 World Continuous Auditing and Reporting Symposium
dc.relation.ispartofdatefrom2013-11-21
dc.relation.ispartofdateto2013-11-22
dc.relation.ispartoflocationBrisbane
dc.relation.ispartofissue2
dc.relation.ispartofvolume8
dc.rights.retentionN
dc.subject.fieldofresearchAccounting, Auditing and Accountability not elsewhere classified
dc.subject.fieldofresearchcode150199
dc.titleAutomating Vendor Fraud Detection in Enterprise Systems
dc.typeConference output
dc.type.descriptionE2 - Conferences (Non Refereed)
dc.type.codeE - Conference Publications
gro.facultyGriffith Business School, Department of Accounting, Finance and Economics
gro.hasfulltextNo Full Text
gro.griffith.authorBest, Peter J.
gro.griffith.authorSingh, Kishore


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record