Abstract: An unsupervised statistical analytics approach to detecting fraud utilizes cluster analysis to identify specific clusters of claims or transactions for additional investigation, or utilizes association rules as tripwires to identify outliers. The clusters or sets of rules define a “normal” profile for the claims or transactions used to filter out normal claims, leaving “not normal” claims for potential investigation. To generate clusters or association rules, data relating to a sample set of claims or transactions may be obtained, and a set of variables used to discover patterns in the data that indicate a normal profile. New claims may be filtered, and not normal claims analyzed further. Alternatively, patterns for both a normal profile and an anomalous profile may be discovered, and a new claim filtered by the normal filter. If the claim is “not normal” it may be further filtered to detect potential fraud.