# Uncovering Insurance Fraud Conspiracy with Network Learning

arXiv: https://arxiv.org/abs/2002.12789 · HTML: https://mlsanity.com/paper/2002.12789/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [Graph Contrastive Learning for Anomaly Detection](https://mlsanity.com/paper/2108.07516/) (2021)
- [Machine Learning for Fraud Detection in E-Commerce: A Research Agenda](https://mlsanity.com/paper/2107.01979/) (2021)
- [Modelling graph dynamics in fraud detection with "Attention"](https://mlsanity.com/paper/2204.10614/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [SENSEi: Input-Sensitive Compilation for Accelerating GNNs](https://mlsanity.com/paper/2306.15155/) (2023)
- [Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks](https://mlsanity.com/paper/2406.02979/) (2024)
- [Explainable Fraud Detection with GNNExplainer and Shapley Values](https://mlsanity.com/paper/2509.12262/) (2025)

## Abstract (arXiv)

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.