Abstract
The insurance industry faces increasing pressure to optimize claims processing efficiency while minimizing claim leakage and fraud. Traditional rule-based and manual approaches are reaching their limits, particularly as market dynamics such as claims inflation and customer demand for faster, self-service experiences accelerate. To address these challenges, we present an AI-driven claims assessment framework that integrates computer vision and generative AI to support human interpretation of visual and textual evidence in vehicle damage claims.
The proposed system comprises two modules: Motor Damage Assessment for analyzing vehicle body damage and Glass Damage Assessment for evaluating windshield damage. The Motor Damage Assessment consists of a vision branch, which analyzes uploaded images to detect damaged components, and a cost estimate branch, which extracts and structures repair operations from cost documents. On top of this architecture, two configurable business levers: Unexpected Actions and Pre-existing Damage are implemented to detect inconsistencies between visual and cost data. This enables automated identification of unnecessary repairs and multi-event damages. The Glass Damage Assessment module determines whether a windshield requires complete replacement or can be safely repaired by analyzing the size, type, and location of detected damage. By combining image-based reasoning, document understanding, and AI-driven optimization, the system significantly improves claims transparency and efficiency. It empowers insurers to automate decision-making, reduce fraudulent or excessive repair costs, and accelerate settlements, ultimately enhancing both operational performance and customer satisfaction.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "AI-Based Claims Assessment for Motor Vehicle Damage Detection", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/8989