  Insurance fraud occurs when policyholders file claims that are exaggerated or
based on intentional damages. This contribution develops a fraud detection
strategy by extracting insightful information from the social network of a
claim. First, we construct a network by linking claims with all their involved
parties, including the policyholders, brokers, experts, and garages. Next, we
establish fraud as a social phenomenon in the network and use the BiRank
algorithm with a fraud specific query vector to compute a fraud score for each
claim. From the network, we extract features related to the fraud scores as
well as the claims' neighborhood structure. Finally, we combine these network
features with the claim-specific features and build a supervised model with
fraud in motor insurance as the target variable. Although we build a model for
only motor insurance, the network includes claims from all available lines of
business. Our results show that models with features derived from the network
perform well when detecting fraud and even outperform the models using only the
classical claim-specific features. Combining network and claim-specific
features further improves the performance of supervised learning models to
detect fraud. The resulting model flags highly suspicions claims that need to
be further investigated. Our approach provides a guided and intelligent
selection of claims and contributes to a more effective fraud investigation
process.
