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Catastrophe (CAT) Modeling

Using data and simulation to estimate potential losses from catastrophic events — hurricanes, wildfires, earthquakes — to inform pricing, risk selection, and.

technicalPublished 2026/06/05

FAQs

What is catastrophe modeling?
Simulating potential losses from catastrophic events like hurricanes and wildfires to inform pricing, risk selection, reinsurance, and capital planning.
How is AI improving CAT modeling?
Granular property data and climate data let models assess individual-property peril risk precisely, beyond coarse zip-code averages.

Related Terms

  • Risk Scoring

    The use of data and models to assign a numeric score representing a risk's likelihood or severity of loss, used to automate triage, pricing, and underwriting.

  • Data Enrichment

    Augmenting a record with additional data from external sources — to pre-fill submissions, validate information, or improve risk assessment — reducing manual.

  • Ratemaking

    The actuarial process of determining insurance prices (rates) based on expected losses, expenses, and profit.

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Catastrophe (CAT) modeling estimates the potential losses an insurer faces from catastrophic events — hurricanes, wildfires, earthquakes, floods, severe storms. Because such events are rare but enormously costly, and because historical data alone can't capture all plausible scenarios, CAT models simulate many possible events to estimate the probability and magnitude of losses across a portfolio.

CAT modeling is essential for property insurers and reinsurers. A carrier writing coastal property needs to understand its hurricane exposure — not just expected annual losses, but tail risk: the rare catastrophic year that could threaten solvency. CAT models inform how much to charge, how much exposure to accept in a region, how much reinsurance to buy, and how much capital to hold. Getting it wrong risks either uncompetitive pricing or catastrophic undercapitalization.

AI and expanded data are sharpening CAT modeling. Granular property data — roof condition, construction details, precise location, defensible space for wildfire — lets models assess individual property risk far more precisely than zip-code-level averages. Climate data improves forward-looking estimates as risk patterns shift. Specialized property-risk AI quantifies perils like wildfire and hail at the individual-property level, feeding more accurate underwriting and pricing.

For property carriers and reinsurers, CAT modeling is core risk infrastructure, increasingly important as climate change alters peril patterns. For agents writing property in exposed areas, it explains carrier behavior — tightening appetite, rising prices, and non-renewals in catastrophe-prone regions trace back to how carriers model and price CAT risk.