Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers' compensation fraud

development of new data analytical methods remains a crucial factor in the combat against insurance fraud. Methods rooted in the research field of anomaly detection are considered as promising candidates for this purpose. Commonly, a fraud data set contains both numeric and nominal attributes, where, due to the ease of expressiveness, the latter often encodes valuable expert knowledge. For this reason, an anomaly detection method should be able to handle a mixture of different data types, returning an anomaly score meaningful in the context of the business application. We propose the iForest(CAD) approach that computes conditional anomaly scores, useful for fraud detection. More specifically, anomaly detection is performed conditionally on well-defined data partitions that are created on the basis of selected numeric attributes and distinct combinations of values of selected nominal attributes. In this way, the resulting anomaly scores are computed with respect to a reference group of interest, thus representing a meaningful score for domain experts. Given that anomaly detection is performed conditionally, this approach allows detecting anomalies that would otherwise remain undiscovered in unconditional anomaly detection. Moreover, we present a case study in which we demonstrate the usefulness of our proposed approach on real world workers' compensation claims received from a large European insurance organization. As a result, the iForest(CAD) approach is greatly accepted by domain experts for its effective detection of fraudulent claims.