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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <link rel="icon" type="image/ico" href="https://it-in-industry.com/index_files/icon.gif"/> <title>Detecting Fraud Using Transaction Frequency Data | Khan | INFORMATION TECHNOLOGY IN INDUSTRY</title> <meta name="description" content="Detecting Fraud Using Transaction Frequency Data"> <meta name="keywords" content="鈥攁nomaly detection; enterprise resource planning systems; fraud detection"> <meta name="DC.Contributor.Sponsor" xml:lang="en" content=""> <meta name="DC.Creator.PersonalName" content="Roheena Khan"> <meta name="DC.Creator.PersonalName" content="Andrew Clark"> <meta name="DC.Creator.PersonalName" content="George Mohay"> <meta name="DC.Creator.PersonalName" content="Suriadi Suriadi"> <meta name="DC.Date.created" scheme="ISO8601" content="2014-10-27"> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2014-10-27"> <meta name="DC.Date.issued" scheme="ISO8601" content="2014-10-27"> <meta name="DC.Date.modified" scheme="ISO8601" content="2014-10-27"> <meta name="DC.Description" xml:lang="en" content="Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. In this paper, we present a fraud detection method which detects irregular frequency of transaction usage in an Enterprise Resource Planning (ERP) system. We discuss the design, development and empirical evaluation of outlier detection and distance measuring techniques to detect frequency-based anomalies within an individual user鈥檚 profile, relative to other similar users. Primarily, we propose three automated techniques: a univariate method, called Boxplot which is based on the sample鈥檚 median; and two multivariate methods which use Euclidean distance, for detecting transaction frequency anomalies within each transaction profile. The two multivariate approaches detect potentially fraudulent activities by identifying: (1) users where the Euclidean distance between their transaction-type set is above a certain threshold and (2) users/data points that lie far apart from other users/clusters or represent a small cluster size, using k-means clustering. The proposed methodology allows an auditor to investigate the transaction frequency anomalies and adjust the different parameters, such as the outlier threshold and the Euclidean distance threshold values to tune the number of alerts. The novelty of the proposed technique lies in its ability to automatically trigger alerts from transaction profiles, based on transaction usage performed over a period of time. Experiments were conducted using a real dataset obtained from the production client of a large organization using SAP R/3 (presently the most predominant ERP system), to run its business. The results of this empirical research demonstrate the effectiveness of the proposed approach."> <meta name="DC.Format" scheme="IMT" content="application/pdf"> <meta name="DC.Identifier.pageNumber" content="79-88"> <meta name="DC.Identifier.URI" content="http://it-in-industry.com/issue/archive/32.html"> <meta name="DC.Language" scheme="ISO639-1" content="en"> <meta name="DC.Rights" content=" Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 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"> <meta name="DC.Source" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="DC.Source.ISSN" content="2203-1731"> <meta name="DC.Source.Issue" content="3"> <meta name="DC.Source.URI" content="http://www.it-in-industry.com"> <meta name="DC.Source.Volume" content="2"> <meta name="DC.Subject" xml:lang="en" content="anomaly detection"> <meta name="DC.Subject" xml:lang="en" content=" enterprise resource planning systems"> <meta name="DC.Subject" xml:lang="en" content="fraud detection"> <meta name="DC.Title" content="Detecting Fraud Using Transaction Frequency Data"> <meta name="DC.Type" content="Text.Serial.Journal"> <meta name="DC.Type.articleType" content="Peer-Reviewed Articles"> <meta name="gs_meta_revision" content="1.1"> <meta name="citation_journal_title" content="INFORMATION TECHNOLOGY IN INDUSTRY"> <meta name="citation_issn" content="2203-1731"> <meta name="citation_author" content="Roheena Khan"> <meta name="citation_author_institution" content="Science and Engineering Faculty Queensland University of Technology"> <meta name="citation_author" content="Andrew Clark"> <meta name="citation_author_institution" content="Science and Engineering Faculty Queensland University of Technology"> <meta name="citation_author" content="George Mohay"> <meta name="citation_author_institution" content="Science and Engineering Faculty Queensland University of Technology"> <meta name="citation_author" content="Suriadi Suriadi"> <meta name="citation_author_institution" content="Science and Engineering Faculty Queensland University of Technology"> <meta name="citation_title" content="Detecting Fraud Using Transaction Frequency Data"> <meta name="citation_date" content="2014/10/14"> <meta name="citation_volume" content="2"> <meta name="citation_issue" content="3"> <meta name="citation_firstpage" content="79-88"> <meta name="citation_abstract_html_url" content="http://it-in-industry.com/issue/archive/32.html"> <meta name="citation_language" content="en"> <meta 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