  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.
