Insurance fraud detection: A statistically validated network approach

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.

Detecting insurance fraud using supervised and unsupervised machine learning

“…Duval et al (2023) further use unsupervised learning, including isolation forests, to derive anomaly profiles of driving behavior and identified a predictive relationship to the probability of automobile insurance claims. Tumminello et al (2023) develop filter rules to identify the criminal infrastructures of fraudsters in extensive networks.…”