Claims fraud detection with uncertain labels

Abstract

Insurance fraud is a non self-revealing type of fraud. The true historical labels (fraud or legitimate) are only as precise as the investigators’ efforts and successes to uncover them. Popular approaches of supervised and unsupervised learning fail to capture the ambiguous nature of uncertain labels. Imprecisely observed labels can be represented in the Dempster–Shafer theory of belief functions, a generalization of supervised and unsupervised learning suited to represent uncertainty. In this paper, we show that partial information from the historical investigations can add valuable, learnable information for the fraud detection system and improves its performances. We also show that belief function theory provides a flexible mathematical framework for concept drift detection and cost sensitive learning, two common challenges in fraud detection. Finally, we present an application to a real-world motor insurance claim fraud.