Insurance fraud detection: A statistically validated network approach
基于二分网络构建调查系统，通过概率模型过滤规则并测试社区检测方法，提出可疑结构预警指标，应用于意大利反欺诈数据库并与司法调查中的欺诈案例对比验证。
Abstract Fraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out‐of‐sample fraud scams under investigation by the judicial authorities.