This paper addresses how Generative AI-mediated policy framing influences insurance outcomes and the broader adjudication ecosystem. We explore a hybrid decision-making framework that combines an XGBoost model for initial claim likelihood prediction and OpenAI’s GPT-4 for policy application. This approach is tested using two distinct policies: a contextual risk-benefit assessment (CRBA) and an equal needs and fairness evaluation (ENFE). The study specifically analyzes how GenAI’s interpretation and application under these different policy framings impact claim approval rates and associated financial liabilities. Furthermore, we examine the broader implications of integrating this AI-driven framework for multiple stakeholders involved in the adjudication process, including ethical considerations.