# Social network analytics for supervised fraud detection in insurance

arXiv: https://arxiv.org/abs/2009.08313 · HTML: https://mlsanity.com/paper/2009.08313/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Influencer Detection with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2211.09664/) (2022)
- [INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2307.08131/) (2023)
- [An engine to simulate insurance fraud network data](https://mlsanity.com/paper/2308.11659/) (2023)
- [Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review](https://mlsanity.com/paper/2503.24259/) (2025)
- [Consistency of Selection Strategies for Fraud Detection](https://mlsanity.com/paper/2509.18739/) (2025)
- [Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection](https://mlsanity.com/paper/2510.05676/) (2025)
- [Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors](https://mlsanity.com/paper/2604.21893/) (2026)

## Abstract (arXiv)

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.