Cluster Analysis is a useful technique for grouping data points such that points within a single group or cluster are similar, while points in different groups are different. The objective of this study is to examine the possibility of using clustering technology for auditing. Automating fraud filtering can be of great value to continuous audits. In the first paper, cluster analysis is used to group transactions from a transitory account of a large international bank. Transactions are clustered based on the open comments field. Major types of transactions are discovered. These results provide a new knowledge about the nature of transactions that flow into transitory accounts. In the second paper, cluster analysis is applied to wire payments within an insurance company. Different anomaly detection techniques are examined. No wire transfer is flagged by all techniques. These results do not necessarily indicate that there is no real anomaly in the dataset, but that different assumptions, parameters or settings should be examined. In the third paper, cluster analysis is applied to group life insurance claims. Individual claims which have significantly different characteristic from other members in the same cluster as well as clusters which comprise of less than 2% of the population are identified as possible anomalies. Moreover, rule-based detection techniques are used to assist internal auditors in selecting claims for further investigation. Cluster analysis and rule-based detection can be combined for the efficiency and effectiveness of the implementation by internal auditors. Cluster analysis has been used extensively in marketing as a way to understand market segments and customer behavior. This study examines the application of cluster analysis in the accounting domain. It can be used for exploratory data analysis (EDA), but also can be used for anomaly detection (i.e. for audit purposes). The results provide a guideline and evidence for the potential application of this technique in the field of audit.
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