Tarek Zari

Reliable auto insurance fraud detection using boosting and deep learning models through comprehensive predictive performance, calibration, statistical significance, and economic impact

A robust evaluation framework for identifying auto insurance fraud that incorporates boosting-based classifiers, advanced deep tabular architectures, and adaptive resampling methods is presented, showing that the optimal model depends on the target objective, whether predictive accuracy or cost-sensitive fraud detectio...