Explainable AI-Based Vehicle Damage Assessment and Insurance Decision Support System

Vehicle insurance claim processing still depends heavily on manual inspection by human surveyors, which is slow, subjective, and difficult to scale. Existing deep learning systems largely stop at detecting or segmenting damage, and offer no repair cost estimation, no explanation of their predictions, and no clear recommendation on the claim itself. This paper proposes an Explainable AI-based Vehicle Damage Assessment and Insurance Decision Support System that connects damage detection and segmentation, damage type classification, repair cost estimation, fraud risk assistance, and a claim recommendation with a confidence score into one pipeline. Explainability is built in through Grad-CAM, which highlights the image regions behind a prediction, and SHAP/LIME, which expose the factors that drive the cost and decision outputs. The system finally generates an explainable report for both customers and insurance staff. The design is based on a survey of recent work on vehicle damage assessment and is planned for implementation using YOLOv11, OpenCV, and the CarDD dataset together with a custom vehicle image dataset.