Abnormal Pattern Prediction: Detecting Fraudulent Insurance Property Claims with Semi-Supervised Machine-Learning

Abstract

Abnormal pattern prediction has received a great deal of attention from both academia and industry, with various applications (e.g., fraud, terrorism, intrusion detection, etc.). In practice, many abnormal pattern prediction problems are characterized by the simultaneous presence of skewed data, a large number of unlabeled data and a dynamic and changing pattern. In this paper, we propose a methodology based on semi-supervised techniques and we introduce a new metric – the Cluster-Score – for fraud detection which can deal with these practical challenges. Specifically, the methodology involves transmuting unsupervised models into supervised models using the Cluster-Score metric, which defines an objective boundary between lusters and evaluates the homogeneity of the abnormalities in the cluster construction. The objectives are to increase the number of fraudulent claims detected and to reduce the proportion of claims investigated that are, in fact, non-fraudulent. The results from applying our methodology considerably improved these objectives. The experiments were performed on a real world data-set and are the results of building a fraud detection system.