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

摘要

The 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.