Geospatial data is becoming a core input in insurance underwriting, particularly for assessing property-level and climate-related risks. At InsurTech NY Spring Conference last week, one data point stood out: only about 30% of insurers are using geospatial and location intelligence in underwriting. A large share of underwriting decisions are still being made without a clear, spatial understanding of risk.
The drivers behind that shift are becoming clearer. Higher-resolution imagery now supports property-level risk scoring at scale. More frequent revisit cycles are replacing static snapshots with continuous monitoring. And broader access to spectral data beyond the visible spectrum is enabling more specific signals across different perils. Together, these changes are making geospatial data more central to how risk is understood.
As those capabilities improve, the gap becomes harder to sustain. Climate and environmental conditions are growing more localized and more variable, and static or partial views of exposure don’t hold up in that context.
Geospatial data is starting to move into the core of how risk is assessed. That was clear in the panel on geospatial data for better risk selection, where EarthDaily’s Margaret Williams joined peers from across the industry. The discussion stayed close to what teams are already using, how workflows are changing, and where things are working or breaking down inside underwriting.
Better visibility into risk changes how decisions are made. When risk can be observed at a higher level of detail, pricing tends to follow that detail. In some cases, that brings premiums closer to actual exposure. In others, it leads to different underwriting outcomes, including stepping away from certain risks altogether.
That tension was hard to ignore in the discussion. Greater precision improves underwriting, but it also defines more clearly what falls inside or outside acceptable risk. Over time, that boundary can tighten. As data improves, fewer assumptions are needed, and decisions become more explicit. In an environment where climate-related risks are increasing, that shift has real consequences for how much of the market remains insurable.
One thing that became clear during the panel is how easily better data can be used in a very narrow way. If geospatial data is primarily used to identify where not to insure, then accuracy improves, but the overall market can begin to contract.
That pattern is already visible in certain regions. As exposure becomes clearer, some risks become harder to justify under existing models. More precise data makes that visible, but it does not resolve what to do next. When a property is identified as high risk, the process often stops there.
What became clear in the discussion is that better risk selection on its own can reduce the addressable market. If more properties are identified as high risk, more of them fall outside traditional coverage models. The more useful question is whether the same data can be used to identify targeted mitigation and bring some of that risk back into scope. Without that step, better visibility leads to more accurate decisions, but not necessarily better outcomes for coverage.
Geospatial data becomes more useful when it goes beyond identifying exposure and starts to explain how that risk forms. It helps show how exposure develops, how it changes over time, and what factors are contributing to it.
That allows underwriting to move beyond a simple accept-or-decline decision. The more useful question becomes: under what conditions does this risk become manageable?
Getting there means working with the data a bit differently. It’s less about a one-time assessment and more about watching how things change. Flood patterns shift. Land use changes. Conditions on the ground don’t stay still, and you need data that reflects that.
That’s where the design philosophy behind EarthDaily’s constellation comes in. Rather than optimizing for resolution or tasking flexibility alone, the system is built around the requirements of time-series analysis. Imaging is done under consistent conditions — same geometry, same time of day — reducing the variability between passes that can otherwise distort change detection.
For underwriting, that consistency matters when tracking how risk evolves over time, whether it’s wildfire exposure or other environmental factors across a policy lifecycle.
That continuous view needs to be translated into something underwriting teams can actually use. At EarthDaily, consistent, comparable measurements are turned into analytics that support property-level risk assessment, track how exposure is evolving over time, and help identify where coverage remains viable within areas that might otherwise be broadly classified as high risk.
It’s easy to assume that better data leads to tighter underwriting. That can happen, especially if the focus stays on filtering risk more precisely.
There are other possibilities as well. More detailed and consistent data can improve confidence in where coverage remains viable. It can support more tailored pricing and enable new product structures, including parametric approaches. In some cases, it can bring risks into the insurable space that might otherwise be excluded under more generalized models.
What happens next depends less on the data itself and more on how it’s used. Precision can narrow decisions, but it can also make them more informed.
Geospatial data is becoming a standard part of underwriting. What matters now is how it’s applied. A narrow use of that data leads to clearer boundaries and, in many cases, more exclusions. A broader use supports better pricing, more targeted mitigation, and a more flexible approach to coverage as risk conditions change.
The capability is already here. What matters now is how it’s used in practice, and what that means for the insurance market going forward.
To see how this translates into underwriting decisions in practice, including how continuous, large-area observation supports risk assessment across asset portfolios, explore EarthDaily’s Ascend platform for unified environmental risk intelligence.