Comparative Analysis of Vehicle Insurance Fraud Detection using EfficientNet, ResNet50, and MobileNet

A study provides detailed test data comparing three leading edge deep learning models, EfficientNet, ResNet50 and MobileNet, determining how well they can detect insurance fraud in automobiles. Its goal is to establish new norms for effective identification of fraudulent claims in the field of vehicle insurance. The study uses a dataset of approximately 11,500 images showing damaged vehicles to train and evaluate the performance of these models. The paper focuses on the utmost importance of insuring against fraud through image analysis, a key means of preventing insurance company financial losses. EfficientNet notches an impressive 99% on accuracy, ResNet50 has robust 98% and MobileNet is nipping the heels of EfficientNet at 99%. These high accuracy rates demonstrate impressively just how well these advanced deep learning models can find the faint symptoms of possible insurance fraud from within images damaged automobile wrecks. As well as showing performance of these models, this study contributes to the debate in insurance about stepping up security. Results suggest that using sophisticated neural network architectures could greatly improve anti-fraud strategies, not only in insurance but also in more general areas of image-based fraud detection, which is essential for developing countries.