An expert system for detecting automobile insurance fraud using social network analysis

TL;DR

This paper introduces an expert system utilizing social network analysis and a novel iterative assessment algorithm to detect groups of automobile insurance fraudsters effectively, emphasizing the importance of data representation.

Contribution

The paper presents a new expert system with a social network-based approach and the Iterative Assessment Algorithm for detecting insurance fraud groups.

Findings

Efficient detection of fraud groups demonstrated on real data.

Network representation improves detection accuracy.

The system addresses practical detection challenges.

Abstract

The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, \textit{Iterative Assessment Algorithm} (\textit{IAA}), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.