Ascend AI Wildfire Probability Mode:
Current Conditions, Not Historical Data

Traditional CAT models rely on historical fire perimeters and outdated fuel maps, neither of which reflect current conditions. EarthDaily's AI Wildfire Probability Model assesses risk based on what's actually on the ground now: current fuel loads, soil moisture, vegetation health, and biomass.

The model is satellite-derived, using imagery from the EarthDaily Constellation and other sources to deliver regular updates on wildfire probability and fuel hazard. The result is wildfire risk intelligence that keeps pace with changing landscapes rather than assuming the future will look like the past.

What the Model Outputs

Wildfire burn probabilities updated at least monthly, and more frequently during elevated risk periods.

Fuel hazard scores reflecting current vegetation and biomass conditions.

Change detection datasets identifying areas where wildfire probability or fuel hazard has shifted over time.

Contributing factor data including soil moisture anomaly, vegetation vigor, fire weather index trends, dominant wind direction, and terrain.

Capabilities
Intelligence Across the Insurance Value Chain

EarthDaily's AI Wildfire Probability Model is not a single-use underwriting score. It delivers actionable intelligence across every function in the insurance value chain, from new business through claims.

Underwriting

Write where others won't. Monthly-updated risk signals give your underwriters clear, explainable insights, not black-box scores, to differentiate risk within the zip codes and fire zones your competitors treat as uniform.

Transparent contributing factors. Every score is backed by observable data including soil moisture, vegetation vigor, terrain, and fuel loads, giving underwriters defensible rationale for pricing and declination decisions.

Risk Accumulation and Portfolio Management

Know your exposure before it knows you. Overlay your TIV against current vegetation and moisture conditions to translate portfolio concentration into a precise, actionable exposure figure that keeps pace with actual fire season dynamics, not last year's data.

Scenario modeling that reflects today's reality. Run realistic disaster scenarios grounded in today's fuel loads and moisture conditions, not historical fire analogs that may no longer reflect how wildfires behave in a changing climate.

Sales and Distribution

Grow where others are pulling back. The model identifies pockets of acceptable risk within broadly high-risk regions, giving you the granular signal to write where competitors won't.

Strengthen agent relationships. Agents get transparent, data-driven explanations for pricing and declination decisions instead of black-box answers. In states like California where regulators pressure carriers to maintain market availability, current-conditions intelligence gives you the methodology to prove responsible underwriting.

Reserving and Financial Planning

Stay ahead of your reserves. Monthly updates give your actuarial team a forward-looking signal to adjust loss reserves proactively, before a fire season materializes into losses.

Accelerate post-event estimation. Burn perimeter and severity data identifies which policyholders sit within or adjacent to the burn area, accelerating IBNR estimation. For carriers with SEC reporting or ORSA obligations, the same data supports defensible wildfire risk disclosures.

Claims

Know your exposure before it knows you. Overlay your TIV against current vegetation and moisture conditions to translate portfolio concentration into a precise, actionable exposure figure that keeps pace with actual fire season dynamics, not last year's data.

Scenario modeling that reflects today's reality. Run realistic disaster scenarios grounded in today's fuel loads and moisture conditions, not historical fire analogs that may no longer reflect how wildfires behave in a changing climate.

Real-World Impact

Use Case:
E&S Carrier: Writing Profitably in Fire-Prone Markets

 

Challenge:
Standard carriers are exiting wildfire-exposed geographies, and submissions are flowing into E&S desks at increasing volume. But the carrier's existing wildfire scores treat entire zip codes and fire zones as uniform risk, forcing underwriters to either decline broadly or write without the granularity to differentiate good risks from bad ones.

Solution:
EarthDaily's AI Wildfire Probability Model provides monthly-updated, property-level wildfire burn probabilities and fuel hazard scores. Underwriters identify pockets of acceptable risk within broadly high-risk regions, supported by transparent contributing factor data including soil moisture, vegetation vigor, and terrain. Agents receive data-driven explanations for pricing decisions rather than black-box outputs.

Outcome:
The carrier writes in markets competitors have abandoned, backed by explainable risk signals that hold up to regulatory scrutiny. Distribution relationships strengthen as agents receive clear, defensible rationale for every decision. Reinsurance negotiations improve because the carrier demonstrates current-conditions wildfire intelligence rather than static model outputs.

Use Case:
Underwriting Flood Risk with Satellite Evidence

Challenge:
The carrier's CAT model was calibrated on historical fire patterns and no longer reflects current fire regimes. Actuarial and underwriting teams know the outputs are drifting, but have no independent signal to challenge them. Portfolio concentration risk is assessed annually against data that is already stale by the time it's published.

Solution:
The carrier layers EarthDaily's monthly wildfire burn probabilities and fuel hazard data alongside existing CAT model outputs. Where the two diverge, the underwriting team has an objective, satellite-derived check on model accuracy. Portfolio managers overlay TIV against current vegetation and moisture conditions for a real-time view of accumulation exposure.
Outcome:
The carrier identifies concentration risks that static models missed, adjusts reserves proactively ahead of fire season, and enters reinsurance renewals with a precise, science-based view of portfolio exposure. CAT model limitations are quantified rather than assumed, and capital planning decisions reflect current conditions rather than historical analogs.

FAQs

A satellite-derived wildfire risk model that delivers monthly-updated wildfire burn probabilities, fuel hazard scores, change detection datasets, and contributing factor data. It uses imagery from the EarthDaily Constellation and other sources to assess risk based on current ground conditions rather than historical fire patterns.

At least monthly, with more frequent updates during elevated risk periods. Daily satellite capture from the EarthDaily Constellation enables this refresh cadence.

The model incorporates soil moisture anomaly, vegetation vigor, fire weather index trends, dominant wind direction, terrain, fuel loads, and biomass to determine wildfire burn probability and fuel hazard.

CAT models rely on historical fire perimeters and loss patterns to estimate future aggregate losses. EarthDaily's model assesses current conditions, including live fuel loads, soil moisture, and vegetation health, providing a forward-looking view of risk that reflects today's landscape rather than historical analogs.

Yes. The model is designed to complement existing CAT workflows, providing an independent, satellite-derived signal that teams can use to validate, challenge, or supplement CAT model outputs. It does not require a rip-and-replace of existing tools.

Burn perimeter and burn severity maps scored on a 0 to 1 scale. Severity data correlates with structural damage and supports claims triage, IBNR estimation, and policyholder outreach prioritization.

Yes. The model outputs are transparent and science-based, backed by satellite-derived contributing factor data. This provides a defensible methodology for wildfire risk disclosures, ORSA obligations, SEC reporting, and reinsurance negotiations.

See What Current-Conditions Wildfire Intelligence Reveals About Your Book

  • Overlay your portfolio against monthly-updated wildfire burn probabilities and fuel hazard scores
  • Compare model outputs against your current CAT model for divergence analysis
  • Evaluate post-event data using burn perimeter and severity maps from recent fires
  • Assess the opportunity in markets competitors have exited using property-level risk differentiation