Resampled Cost-Sensitive Operational Machine learning Ensemble (ROME) framework for Car Insurance Fraud Detection

This study introduces a novel approach to enhance car insurance fraud detection through the ROME framework, which integrates resampling techniques with cost-sensitive machine learning. The philosophy behind this method stems from addressing two critical challenges in fraud detection: the imbalance in datasets and the high cost associated with misclassifying fraudulent cases. The resampling method ensures balanced data representation, while the cost-sensitive approach prioritizes reducing the misclassification impact, aligning with the industry's goal of minimizing financial losses. This hybrid strategy marks a significant advancement in fraud detection. The model was tested on real-world car insurance data, achieving an impressive F۱ Measure of ۷۶.۳۲%, outperforming the CatBoost baseline by ۳۱.۲۵%. These results highlight the effectiveness of the combined approach in enhancing detection accuracy, equipping insurers with a robust tool for improved risk management. The findings offer substantial contributions to the insurance industry by bolstering the reliability and efficiency of fraud detection systems.

کلیدواژه ها:

Fraud detection ، Car insurance ، Resampling ، Cost-sensitive ، Machine learning