Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach

This 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.