A Blockchain-Based Fraud Detection and Vehicle Damage Assessment System Using Machine Learning and Computer Vision

Abstract

Car insurance is a cornerstone of modern society, offering crucial financial protection in the event of accidents and vehicle-related damage. However, this intricate system is now grappling with a significant challenge: fraud manifesting in various forms, including staged accidents, fraudulent claims, and collusion between individuals, and it poses a serious threat to the integrity and long-term viability of car insurance. In this paper, we will propose an innovative approach to fraud detection in car insurance and reimbursement estimation. The proposed approach makes significant contributions to the field by introducing a new dataset of 5,483 images with corresponding labels. Fraud detection is performed using the XGBoost Classifier, which is known for its robustness in handling complex classification tasks. Damage detection is carried out using the Mask R-CNN model, enabling precise identification and segmentation of vehicle damages. The system integrates structured data fraud detection with image-based damage assessment, where Mask R-CNN results serve as an additional validation factor. This end-to-end approach enhances fraud detection accuracy by combining data-driven insights with visual evidence for more reliable claim verification. These advancements contribute to improving the accuracy and efficiency of automated fraud detection and reimbursement estimation systems.