AI-Led Blueprint for Automated Claims Adjudication

This case study presents a detailed examination of an AI-led blueprint developed for automating the claims adjudication process by a leading US-based Fortune 100 personal lines insurer. The document outlines the challenges faced in the current claims evaluation process, which relies heavily on manual review of vehicle damage images, resulting in inefficiencies and poor customer experiences. It describes the introduction of AI-powered Computer Vision techniques aimed at automating the evaluation of vehicle damage and estimating repair costs, thereby facilitating faster claim handling. The case study details a structured approach comprising three key steps: use case development, use case prioritization, and phased roadmap development. It highlights the identification of over 100 use cases, prioritizing 36 high-impact scenarios based on damage categories and severity. The solution architecture is also documented, explaining the use of pretrained neural network models for damage detection and repair cost estimation, emphasizing the need for specialized tech infrastructure to support image analytics.