Auto insurance fraud detection: Machine learning and deep learning applications

Abstract

Insurance fraud detection remains a challenging task due to severe data imbalance, evolving fraudulent behaviors, and the high false-negative rates exhibited by several state-of-the-art machine learning models. Traditional approaches often struggle to generalize real-world data and capture complex, non-linear feature interactions in insurance claims. This study aims to improve fraud detection performance by leveraging recent advances in deep learning. A comprehensive comparison between traditional machine learning models and deep learning techniques is performed on two distinct datasets using resampling strategies. The study proposes three convolutional neural network-based architectures to improve detection accuracy. Furthermore, a hybrid machine learning deep learning (ML-DL) framework is introduced to more effectively leverage discriminative features. Experimental results demonstrate that deep learning models would vary on each dataset due to the presence of variations in data characteristics, while the proposed hybrid ML–DL model achieves the best overall performance, highlighting its effectiveness in improving fraud prediction accuracy.