Algorithmic Catastrophe Pricing: A Serverless Spatiotemporal Machine Learning Architecture for Evaluating Climate Risk and Disaster Insurance Retreat

The increasing frequency of severe climate anomalies and natural disasters has destabilized global insurance markets, precipitating a widespread retreat of private disaster insurance. Financial economists modeling the economics of natural hazard risks are frequently constrained by low-frequency, aggregated historical claims data, which fails to capture the real-time micro-structural shifts in spatial vulnerability. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for dynamic disaster insurance pricing and catastrophe bond valuation. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting and spatial econometric algorithms, the proposed system programmatically ingests high-frequency meteorological, seismic, and property valuation data. The system translates these inputs into a dynamic Spatiotemporal Risk Premium, instantly identifying geographic zones susceptible to imminent private insurance retreat. Preliminary architectural evaluations demonstrate that coupling enterprise cloud infrastructure with spatial econometrics significantly reduces the latency of risk pricing, providing economists and policymakers with a deterministic, highly scalable technological foundation for designing sustainable Public Disaster Insurance systems.