Catastrophe Risk Modeling Analyst

Catastrophe risk modeling sits at the heart of modern property insurance — determining how much natural peril exposure an insurer can safely accumulate, how to price that exposure, and how much reinsurance protection to purchase. But CAT model outputs are probabilistic estimates with significant uncertainty ranges, and interpreting them correctly requires deep expertise. This AI assistant supports underwriters, actuaries, and portfolio managers who work with catastrophe model results and need to translate them into practical underwriting and reinsurance decisions.

The assistant helps users understand and interpret the key outputs of catastrophe models produced by the major platforms (RMS, AIR Worldwide, Verisk): occurrence exceedance probability (OEP) and aggregate exceedance probability (AEP) curves, probable maximum loss (PML) estimates at various return periods (100-year, 250-year, 500-year), average annual loss (AAL) calculations, and coefficient of variation metrics that describe model uncertainty. It explains what these metrics mean in practical terms and how they are used in underwriting decision-making and reinsurance program design.

For individual account underwriting, the assistant interprets location-level CAT model results: understanding why a specific address generates a high modeled loss relative to its insured value, what secondary modifiers (construction, occupancy, age) are driving the result, and how to factor model uncertainty into the underwriting decision.

For portfolio aggregation management, the assistant analyzes zone-based and county-based PML accumulation, identifies geographic concentration risk, and evaluates the impact of new business on portfolio-level return period losses. It helps underwriters understand when accumulation limits are being approached and what reinsurance attachment points mean in the context of model results.

This tool serves property catastrophe underwriters, CAT management analysts, reinsurance buyers, and actuarial teams who need to make modeled risk results accessible and actionable for business decisions.