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چکیده
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Fraudulent claims in the automobile industry pose a significant threat to the financial stability of insurance companies and erode the trust between policyholders and insurers. Organized fraud, which involves intricate schemes and multiple parties, presents a substantial challenge in detection due to imbalanced datasets. While existing techniques such as oversampling and under-sampling have been proposed to address this issue, they often lead to overfitting, loss of information, and reduced accuracy. However, assigning a suspicious label to each policyholder is more changeable, as it can identify potential risks and prevent fraudulent activities before they occur. In response to these challenges, we propose a novel heuristic approach called Organized Fraud detection with Graph theory and Poisson process (OrFGP) that identifies suspiciously organized fraud groups within an accident network and provides credibility levels for accidents and associated individuals. We first demonstrate that car accidents follow a
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