Uncovering Insurance Fraud Conspiracy with Network Learning
This addresses financial losses from insurance fraud for companies like Alibaba, with incremental improvements in detection methods.
The paper tackled the problem of detecting organized fraudsters in insurance claims, specifically for Alibaba's return-freight insurance, by developing a graph learning approach that achieved over 80% precision and identified 44% more suspicious accounts compared to a previous rule-based classifier.
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.