
Find the climate-risk blind spots across your book. Flood, wildfire, and hail don't tell the whole story. Screen nine additional extreme climate perils to surface higher-hazard locations and see where exposure is building across the portfolio.
Nine extreme climate perils
Bring overlooked climate exposures into the risk picture and give your team a broader view of how climate risk can affect a location and where exposure can build up across your portfolio.
Extreme Temperature
Extreme Temperature
Extreme Temperature
Extreme Temperature
Rainfall, Moisture & Snow
Rainfall, Moisture & Snow
Rainfall, Moisture & Snow
Wind & Cyclone
Wind & Cyclone
Standardized hazard screening
Each peril model is built from its own climate data and methodology, then translated into the same 1–10 hazard scale so your team gets science-backed risk signals that are easy to read across properties and portfolios.
The science behind the models
When a score leads to an underwriting referral, a pricing change, or a shift in portfolio appetite, the reasoning shouldn't disappear into a black box. Trace the result back to the methodology, assumptions, and evidence behind it.
Insurance decisions
Use the nine hazard scores to flag properties that need a different underwriting path, test whether existing segments are masking meaningful differences in exposure, and see where hazards are clustering across your book.
Underwriting
Pricing & Actuarial
Portfolio Risk
Access & delivery methods
Some teams need a map-based view. Some need data. Some need scores inside quoting, renewal, or underwriting workflows. Use the same risk intelligence in the way your teams already work.
Use the portal to explore properties, compare risk layers, and create a shared view of exposure for underwriting, risk, and leadership conversations.
Bring Geosapiens' standardized scores into third-party platforms or internal tools, so the risk signal appears right where the decision is made.
Give actuarial, catastrophe modelling, BI, and portfolio teams data outputs they can test, compare, and use inside your internal models, dashboards, and risk workflows.
Bring a sample of locations or a portfolio slice and see which properties stand out, which extreme climate perils are driving the risk, and where exposure is clustering across the portfolio.