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dc.contributor.authorThiprungsri, Sutapat
dc.contributor.authorVasarhelyi, Miklos A.
dc.date.accessioned2012-02-08T13:43:48Z
dc.date.available2012-02-08T13:43:48Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10272/5478
dc.description.abstractThis study examines the application of cluster analysis in the accounting domain, particularly discrepancy detection in audit. Cluster analysis groups data so that points within a single group or cluster are similar to one another and distinct from points in other clusters. Clustering has been shown to be a good candidate for anomaly detection. The purpose of this study is to examine the use of clustering technology to automate fraud filtering during an audit. We use cluster analysis to help auditors focus their efforts when evaluating group life insurance claims. Claims with similar characteristics have been grouped together and small-population clusters have been flagged for further investigation. Some dominant characteristics of those clusters which have been flagged are large beneficiary payment, large interest payment amounts, and long lag between submission and payment.en_US
dc.language.isoengen_US
dc.publisherUniversidad de Huelvaen_US
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherContinuous auditingen
dc.subject.otherCluster analysisen
dc.subject.otherAnomaly detectionen
dc.subject.otherInsurance industryen
dc.titleCluster analysis for anomaly detection in accounting data : an audit approachen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Atribución-NoComercial-SinDerivadas 3.0 España
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