A Framework for Economic Impact Assessment of AI-Enhanced Parametric Insurance for US Climate Disaster Recovery

The escalating frequency and severity of climate disasters in the United States have amplified the urgency for innovative financial risk-transfer mechanisms that can support rapid recovery and long-term resilience. Traditional insurance systems are increasingly strained by unpredictable hazards such as hurricanes, floods, and wildfires, often leading to delayed payouts, underwriting challenges, and affordability concerns for vulnerable communities. In response, parametric insurance characterized by trigger-based payouts tied to predefined environmental indices has emerged as a promising alternative. However, the effectiveness and scalability of parametric insurance hinge on its integration with advanced analytical tools that enhance accuracy, trust, and economic viability. This paper proposes a comprehensive framework for assessing the economic impact of AI-enhanced parametric insurance within the context of U.S. climate disaster recovery. The framework integrates machine learning for improved hazard modeling, natural language processing for regulatory compliance, and explainable AI to increase transparency in payout mechanisms. It evaluates three key domains: (1) direct financial benefits, including faster claims processing and reduced administrative overhead; (2) systemic risk mitigation through predictive analytics and portfolio optimization; and (3) socio-economic outcomes, such as accessibility for underserved populations and contributions to regional resilience. By embedding AI into the operational and governance layers of parametric insurance, the framework seeks to quantify both cost savings and broader macroeconomic benefits. The study concludes by highlighting policy implications for regulators and insurers, emphasizing the role of AI in balancing efficiency, equity, and resilience in climate risk financing. The framework establishes a pathway for future research to operationalize AI-driven insurance models as tools for sustainable disaster recovery.