Hazard Data
Drive better underwriting results with the world’s best hazard datasets.
eBook: An Insurance Underwriters' Guide
Identify, connect, and surface the right data to quickly inform the best decisions with Location Intelligence.
Actionable Hazard Insight
If claims data is the black-and-white information that drives property underwriting, then hazard data is the color that adds depth. Often used as a proxy for risk, hazard data yields decision-making insight by providing discrete information about the condition of a property that may impact its loss outcome.
Hazard data can provide clues about a property’s susceptibility to loss that wouldn’t otherwise be apparent in its historical loss experience.
Benefits
Leverage Our Science Expertise
Better underwriting requires better data, analysis, and expertise. Moody's employs seismologists, meteorologists, and hydrologists so you don’t have to. You can use Moody's Hazard Data to select, evaluate, and price catastrophe risk – bringing deep peril expertise directly to your underwriting specialists.
Ensure Data Consistency Across Departments
Moody's Hazard Data is native to industry-leading Moody's RMS models and can offer valuable insights to you when analyzing risks. Applying the same data within portfolio management, reinsurance decisions, and underwriting ensures that there is a consistent basis for all decisions across the risk management process.
Gain a View of Hazard Across the World
For companies operating in different areas of the world, or those that want to expand into other parts, Moody's offers hazard data across regions to help you evaluate risks broadly, save time, and gain efficiency. Whether you need to evaluate wildfire risk in California, flood risk in the United Kingdom, or earthquake risk in New Zealand, we make this possible via a very performant API.
Out-Select Competitors
Different locations often look the same on paper, but are subject to very different hazards impacting their riskiness. Moody's Hazard Data can drive deep insights into the nature of the risks, enabling you to avoid adverse selection and improve risk decisions.
Hazard Data Examples
- 250-year earthquake shake intensity
- 100-year flood depth, defended and undefended
- Soil condition
- Liquefaction susceptibility
- Flood, wind, wildfire zone
Hazard Data Examples (continued)
- Distance to coast, earthquake fault, wildfire zone
- Number of historical wildfires within 2, 5, 10 kilometers
- First-floor height above ground
- Likelihood of basement presence
- And more
Related Solution
Resources
The Data Difference
The value of data as a driver of business decisions has grown exponentially as the importance of generating sustainable underwriting profit becomes the primary focus for companies in response to recent diminished investment yields. Increased risk selection scrutiny is more important than ever to maintain underwriting margins. High-caliber, insightful risk data is critical for the data analytics that support each risk decision The insurance industry is in a transformational phase where profit margins continue ...
Underwriting With 20:20 Vision
Risk data delivered to underwriting platforms via application programming interfaces (API) is bringing granular exposure information and model insights to high-volume risks The insurance industry boasts some of the most sophisticated modeling capabilities in the world. And yet the average property underwriter does not have access to the kind of predictive tools that carriers use at a portfolio level to manage risk aggregation, streamline reinsurance buying and optimize capitalization. Detailed probabilisti...
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