Discovering Local Subgroups, with an Application to Fraud Detection

Abstract

In Subgroup Discovery, one is interested in finding subgroups that behave differently from the ‘average’ behavior of the entire population. In many cases, such an approach works well because the general population is rather homogeneous, and the subgroup encompasses clear outliers. In more complex situations however, the investigated population is a mixture of various subpopulations, and reporting all of these as interesting subgroups is undesirable, as the variation in behavior is explainable. In these situations, one would be interested in finding subgroups that are unusual with respect to their neighborhood. In this paper, we present a novel method for discovering such local subgroups. Our work is motivated by an application in health care fraud detection. In this domain, one is dealing with various types of medical practitioners, who sometimes specialize in specific patient groups (elderly, disabled, etc.), such that unusual claiming behavior in itself is not cause for suspicion. However, unusual claims with respect to a reference group of similar patients do warrant further investigation into the suspect associated medical practitioner. We demonstrate experimentally how local subgroups can be used to capture interesting fraud patterns.