Catastrophe & Natural Disaster Loss Data
Buy and sell catastrophe & natural disaster loss data data. Hurricane, earthquake, wildfire claims at scale — CAT modeling AI needs real loss data from major events.
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What Is Catastrophe & Natural Disaster Loss Data?
Catastrophe and natural disaster loss data captures financial and economic impact metrics from major events—hurricanes, earthquakes, wildfires, floods, and severe convective storms. This data is critical for insurers, reinsurers, and catastrophe modeling firms that rely on granular, event-level loss information to price risk, manage solvency, and forecast future exposure. The market includes both insured losses (covered by insurance policies) and total economic losses (which often exceed insured amounts by large margins). In 2024, global insured losses reached $137 billion, with economic losses totaling $318 billion—a protection gap of $181 billion. Catastrophe bonds and advanced climate-risk modeling now drive pricing decisions instead of historical loss data alone, making real disaster loss datasets increasingly valuable for scenario analysis and capital planning.
Market Data
$107 billion
Global Insured Catastrophe Losses (2025 Expected)
Source: Reuters / Swiss Re Institute
$137 billion
Global Insured Losses (2024)
Source: Swiss Re
$318 billion
Total Economic Losses (2024)
Source: Swiss Re
$61.3 billion
Global Catastrophe Bond Market (2025)
Source: Artemis
83%
U.S. Share of Global Insured Losses (2025)
Source: Reuters / Swiss Re Institute
Who Uses This Data
What AI models do with it.do with it.
Catastrophe Modeling & AI Development
Machine learning models for disaster impact prediction and economic loss forecasting require granular historical loss data from major events. Models incorporate warming trajectories, land-use change, and infrastructure vulnerabilities to improve scenario analysis beyond traditional historical approaches.
Insurance & Reinsurance Pricing
Insurers and reinsurers use catastrophe loss data for capital planning, solvency calculations, and premium setting. Reinsurers now demand more granular climate-risk modeling from primary insurers, driving tighter terms and higher reinsurance costs.
Climate Risk & Policy Analysis
Governments, emergency planners, and resilience analysts use disaster loss data to assess geographic risk exposure, improve early warning systems, and guide disaster-resistant infrastructure investments across regions.
Catastrophe Bond & Financial Innovation
Investors and insurance-linked securities firms use loss event data to price catastrophe bonds—financial instruments that let investors bet on disaster occurrence. The cat bond market reached $61.3 billion in 2025, nearly doubling since 2020.
What Can You Earn?
What it's worth.worth.
Event-Level Loss Data (Single Disaster)
Varies
Granular claim-level or insured loss figures from major hurricanes, wildfires, or earthquakes command premiums based on event magnitude and recency. LA wildfires (2025) and Hurricanes Helene & Milton (2024) are high-demand datasets.
Historical Loss Database (Multi-Year, Multi-Peril)
Varies
Compiled datasets covering multiple disaster types across regions and years are priced by breadth, granularity, and modeling-readiness. Insurers and CAT modeling firms are primary buyers.
Real-Time or Near Real-Time Loss Feeds
Varies
Streaming or rapid-update loss data from active disaster events commands premium pricing for traders, reinsurers, and catastrophe bond investors.
What Buyers Expect
What makes it valuable.valuable.
Granularity & Completeness
Buyers require detailed claim-level or exposure-level loss data, not just aggregate figures. Economic losses, insured losses, and protection gap (uninsured portion) should all be clearly documented. Data must distinguish between peril types: hurricanes, wildfires, earthquakes, floods, severe convective storms.
Geographic & Temporal Precision
Loss data must be geographically coded (by region, state, county, or zip code) and time-stamped to event occurrence. Reinsurers and modelers use this to map exposure patterns and validate scenario analysis against historical precedent.
Validation & Provenance
Data should reference authoritative sources—insurance industry reports (Swiss Re, Gallagher Re, Aon), regulatory filings, or government disaster databases. Transparent sourcing builds confidence for capital-intensive buyers pricing solvency reserves.
Climate & Infrastructure Context
Modern buyers increasingly demand metadata on warming scenarios, land-use change, and infrastructure vulnerability scores. This supports forward-looking scenario analysis rather than historical-only pricing.
Companies Active Here
Who's buying.buying.
Capital planning, premium pricing, and solvency modeling. Use loss data to recalculate peak risk absorption and tighten terms, raising catastrophe reinsurance costs and demanding more granular climate-risk modeling.
Train machine learning models on historical disaster losses to forecast economic impact and improve scenario analysis. Published datasets (e.g., Kaggle's Global Disaster & Emergency Response dataset) serve researchers and modelers.
Price and manage catastrophe bonds—securities that transfer disaster risk to capital markets. Use loss data to model probability and magnitude of future events. Global cat bond market reached $61.3 billion in 2025.
Plan disaster response, improve early warning systems, and guide resilience infrastructure investment. Use loss data to assess geographic risk and prioritize vulnerable regions across Asia Pacific (11.5% CAGR), Latin America (10.2%), and Middle East & Africa.
FAQ
Common questions.questions.
What types of disasters are included in catastrophe loss data?
The primary peril types are hurricanes, earthquakes, wildfires, severe convective storms (SCS), large-scale urban floods, and droughts. In 2024, Hurricanes Helene and Milton, severe convective storms in the U.S., urban floods globally, and Canada's record insured losses were the top loss drivers.
How does insured loss data differ from total economic loss data?
Insured loss is the amount covered by insurance policies; economic loss is the full financial damage from an event. In 2024, total economic losses were $318 billion while insured losses were $137 billion—a protection gap of $181 billion. This gap is a key metric for insurers and policymakers.
Why are insurers and reinsurers demanding more granular climate-risk modeling?
Catastrophe losses are becoming more frequent and severe, forcing insurers to rethink capital allocation. Rather than pricing solely on historical loss data, they now incorporate warming trajectories, land-use change, and infrastructure vulnerabilities into scenario analysis to forecast future risk exposure.
What is driving growth in the catastrophe bond market?
The global catastrophe bond market reached a record $61.3 billion in 2025, nearly doubling since 2020. Property insurers are increasingly turning to catastrophe bonds as an alternative to traditional reinsurance, allowing them to transfer disaster risk directly to capital market investors.
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