Part of a new industry series Insuring the Future™: Climate Risk Intelligence™ for Insurance Services
Data Foundations: From Climate Models to Insurance-Ready Hazard Layers
Robust Climate Risk Intelligence™ rests on solid data foundations. On the climate side, insurers increasingly draw on ensembles of global and regional climate models, such as those in CMIP6 and summarized in the IPCC’s Sixth Assessment Report, which project how temperature, precipitation, storms, and sea level may evolve under different emissions pathways over the coming decades (IPCC, 2021). To be usable for insurance, these projections must be translated into hazard layers: gridded flood depths for multiple return periods, fire weather indices, peak wind gust maps, or heat metrics such as days above critical temperature thresholds, often at resolutions of tens of meters for key perils.
Exposure Intelligence: Geocoding, Building Attributes, and Business Interruption Drivers
On the insurance side, exposure data quality is equally critical. Accurate geocoding—ideally at rooftop or parcel level—can reveal that two properties on the same street face different risks due to micro topography or drainage. Building attributes such as construction material, roof type, elevation above ground, number of stories, year built, and flood or fire resistance measures strongly influence damage outcomes. For commercial and industrial risks, understanding which locations generate what share of revenue, how long operations can tolerate outages, and where key suppliers and customers are located is essential for modeling business interruption.
Enterprise Governance: Lineage, Version Control, and Data Quality Targets Above 95 Percent
To support enterprise-wide use, these data sources must live in a governed data platform. Lineage and version control should clarify which climate models, scenarios, and exposure snapshots underpin each analysis. Data quality metrics may include the percentage of policies with precise coordinates, where targets above 95 percent are increasingly common, and the share of missing or “unknown” values in key attributes, which insurers may aim to reduce below 5 to 10 percent. Data refresh cycles should be defined for both climate and exposure, as hazard layers are updated and portfolios change.
Trust and Disclosure: Why Audit-Ready Data and Methods Now Matter
Investments in data quality and governance may be less visible than new models, but without them, Climate Risk Intelligence™ will be inconsistent or untrustworthy. Regulators, rating agencies, and boards are beginning to ask not only for climate metrics, but also for evidence that the underlying data and methods meet appropriate standards of accuracy, transparency, and control (TCFD, 2017).
Frequently Asked Questions (FAQs)
- What data is required to build Climate Risk Intelligence™ for insurance? Climate Risk Intelligence™ requires two core data foundations: climate hazard data derived from ensembles of global and regional climate models, and high-quality exposure data describing insured assets. Climate model outputs must be translated into insurance-ready hazard layers, while exposure data must be accurately geocoded and enriched with building, operational, and financial attributes.
- Why are climate model ensembles used instead of a single model? Using ensembles of climate models helps capture uncertainty and reduce reliance on any single model’s assumptions. For insurers, ensemble approaches provide more robust estimates of hazard frequency, severity, and return periods under current and future climate scenarios, which improve pricing, underwriting, and risk management decisions.
- Why is rooftop- or parcel-level geocoding so important for insurers? Fine-resolution geocoding can reveal meaningful differences in risk between nearby properties due to micro-topography, drainage, elevation, or proximity to hazards. These differences materially affect expected losses, underwriting decisions, and capital allocation, particularly for flood, wildfire, and wind risk.
- What data quality metrics matter most for insurance climate risk analytics? Common metrics include the percentage of policies or assets with precise coordinates (with targets above 95 percent increasingly common), the share of missing or “unknown” values in critical attributes, and the freshness of both hazard and exposure data. These metrics help ensure analyses are consistent, explainable, and audit-ready.
- Why do governance, lineage, and version control matter for climate risk disclosures? Regulators, rating agencies, and boards increasingly expect insurers to demonstrate not only climate risk metrics but also how those metrics were produced. Data lineage, scenario, and model version control, and documented assumptions provide transparency, support governance and disclosures, and help ensure Climate Risk Intelligence™ outputs are defensible and trustworthy.
More in the next post on Insuring the Future™: Climate Risk Intelligence™ for Insurance Services…
Ready to get started? To learn how ClimaTwin can help you assess the physical and financial impacts of future weather and climate extremes on your infrastructure assets, capital programs, and investment portfolio, please visit www.climatwin.com today.
© 2026 ClimaTwin Corp. All rights reserved worldwide.ClimaTwin® is a registered trademark of ClimaTwin Corp. The ClimaTwin logos, ClimaTwin Solutions™, Climate Risk Intelligence™, Climate Business Intelligence™, Climate Value at Risk™, Future-proofing assets today for tomorrow’s climate extremes™ are trademarks of ClimaTwin Corp. All trademarks, service marks, and logos are protected by applicable laws and international treaties, and may not be used without prior written permission of ClimaTwin Corp.
###