- The paper introduces an expert system that enhances fraud detection by representing entity relationships through social network analysis.
- It employs a modular design involving network construction, suspicious component detection, and iterative re-assessment of entity attributes.
- Evaluation results show that the Iterative Assessment Algorithm achieved an AUC of 0.9228, outperforming traditional flat data methods.
An Expert System for Detecting Automobile Insurance Fraud Using Social Network Analysis
Introduction
This paper presents a novel approach to the detection of organized automobile insurance fraud through the deployment of an expert system incorporating social network analysis (SNA). Unlike traditional methods that rely predominantly on flat data representations and intrinsic features, this system uses network-based data representations to enhance fraud detection capabilities. The core of the proposed system leverages a custom algorithm, the Iterative Assessment Algorithm (IAA), which assesses both intrinsic entity attributes and the relational structure among entities to detect fraudulent activities.
System Design and Methodology
The system's design is modular, consisting of four primary components: network construction, suspicious component detection, suspect entity detection, and results visualization. Initially, the system constructs various types of networks—such as drivers networks, participants networks, and COPTA networks (Connect Passengers Through Accidents)—from given data, representing relationships between entities like participants, vehicles, and collisions.
Network Construction
Networks are constructed to include vertices representing entities and edges representing relationships. The choice of network type depends on different feature representation strategies, which balance completeness of relation portrayal and practical application in identifying patterns indicative of fraud.
Detection of Suspicious Components
Once networks are constructed, connected components within these networks are identified and evaluated. The task is to flag components as potentially fraudulent based on the evaluation of multiple indicators, capturing structural properties such as density, diameter, and vertex centrality. These evaluations leverage statistical techniques, including PRIDIT analysis, to weight and combine indicator signals without reliance on a priori labeling.
Iterative Assessment Algorithm
Fraudulent entities within the suspicious components are further investigated using the IAA. In this iterative process, entities are continuously re-evaluated by taking into account not only their standalone attributes but also the updated assessment of their network neighbors, effectively propagating suspicion through the network in a controlled iteration process until a convergent suspicion distribution is determined for all entities.
Evaluation and Results
The system's performance was empirically evaluated using a dataset of automobile insurance claims, comprising a mix of labeled known fraud cases and a randomly sampled unlabeled portion. Key metrics such as AUC were used to assess system efficacy in rank ordering entity suspicion scores, demonstrating that the system significantly outperformed baseline methods that utilized simpler machine learning approaches on flat representations.
The evaluation confirmed that the use of networks to represent the relational structure of entities enhances detection accuracy, with the IAA showing robust performance across several fraud indicator configurations. Notably, the system achieved an AUC of 0.9228 when deployed with the IAA using expert-tuned factors, an indication of the system's high efficacy in differentiating between fraudulent and non-fraudulent entities.
Discussion and Conclusion
This research underscores the crucial role of proper data representation in fraud detection systems, with network-based models providing a more expressive and robust platform for identifying complex fraud patterns. The expert system's design facilitates adaptability and extendibility across various relational domains beyond automobile insurance fraud.
Future research will enhance the IAA by facilitating model learning in an unsupervised manner, thereby diminishing the need for predefined domain-specific factors. The framework will be evaluated for diverse types of fraud to test its broader applicability. As the framework relies heavily on relational structures, it positions itself as a versatile tool for domains where entity interactions signify critical investigative leads.
In conclusion, the paper contributes a practical, scalable, and data-efficient solution to combating insurance fraud, showcasing the synergy between AI algorithms and domain-specific expertise in tackling complex real-world problems.
