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Insurance fraud occurs when policyholders file claims that are exaggerated orbased on intentional damages. This contribution develops a fraud detectionstrategy by extracting insightful information from the social network of aclaim. First, we construct a network by linking claims with all their involvedparties, including the policyholders, brokers, experts, and garages. Next, weestablish fraud as a social phenomenon in the network and use the BiRankalgorithm with a fraud specific query vector to compute a fraud score for eachclaim. From the network, we extract features related to the fraud scores aswell as the claims' neighborhood structure. Finally, we combine these networkfeatures with the claim-specific features and build a supervised model withfraud in motor insurance as the target variable. Although we build a model foronly motor insurance, the network includes claims from all available lines ofbusiness. Our results show that models with features derived from the networkperform well when detecting fraud and even outperform the models using only theclassical claim-specific features. Combining network and claim-specificfeatures further improves the performance of supervised learning models todetect fraud. The resulting model flags highly suspicions claims that need tobe further investigated. Our approach provides a guided and intelligentselection of claims and contributes to a more effective fraud investigationprocess.

Social network analytics for supervised fraud detection in insurance