Resampled cost-sensitive Operational Machine learning Ensemble (ROME) framework for car insurance fraud detection
Resampled cost-sensitive Operational Machine learning Ensemble (ROME) framework for car insurance fraud detection
By
Behnam Yousefimehr, Mehdi Ghatee, Ayin Ghazimoradi, Vista Farahifar
has been accepted in “International Journal of Information and Communication Technology Research”.
Abstract
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 F1 Measure of 76.32%, outperforming the CatBoost baseline by 31.25%. 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.
Keywords
Fraud detection; Car insurance; Resampling; Cost-sensitive; Machine learning
Additional source-archive excerpt: OCT study
This retained source-archive excerpt concerns a separate OCT study by Amin Rahmani and Mehdi Ghatee; it is not the reference or access link for the ROME study. The original notice remains below for the record. Its stated free-access deadline has passed. Read the separate OCT study.
Ref:
The full version of the paper “Fast inference in classification of optical coherence tomography (OCT) images for real-time retinal disease diagnosis” by Amin Rahmani & Mehdi Ghatee is now available online in “Biomedical Signal Processing and Control”. It can be downloaded by clicking the following Share Link before February 16, 2025. No sign up, registration, or fees are required.
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