Underwriters rely on coarse postal codes that fail to capture localized flood risk analytics, storm surge elevation gradients, or wildfire boundary exposures.
Insurance & climate-risk analytics
Measure Exposure at the Location and Portfolio Levels
Spatial catastrophe analytics and climate risk modeling for insurers, reinsurers, and risk teams — specializing in high-resolution flood risk analytics, wildfire risk modeling, storm surge coastal inundation, and location-level property hazard scoring.
// The buying trigger
You probably need this when…
Portfolio totals hide local accumulation and concentration near shared hazards.
Hazard scores cannot be traced through version, resolution, spatial join, and aggregation logic.
Catastrophe modeling location intelligence is compromised by imprecise rooftop geocoding and delayed post-event satellite footprints.
// Concrete scope
What the engagement delivers
Every item is tied to an acceptance owner and evidence. Final scope is confirmed after data, security, and integration review.
Location, address, coordinate, value, and hazard-source diagnostic
Geocoding confidence, deduplication, and exception workflow
Versioned hazard enrichment and exposure methodology
Accumulation, concentration, scenario, event-footprint, or portfolio dashboard
Reconciliation, lineage, uncertainty, access control, documentation, and handover
// Delivery process
From evidence to operational handover
- 01
Define the risk decision
Specify peril, location grain, aggregation, threshold, users, and approved interpretation.
- 02
Repair location quality
Standardize, geocode, score confidence, deduplicate, and route exceptions.
- 03
Build traceable exposure
Version hazard sources and document spatial joins, assumptions, and aggregation.
- 04
Validate and operationalize
Reconcile totals, back-test known events, and integrate reporting or response workflows.
// Proof before promises
How value is verified
No vanity accuracy number. Evidence is chosen around the operational decision and agreed before delivery starts.
- ✓Geocode match, confidence, exception, duplicate, and geographic-assignment rates
- ✓Exposure reconciliation from location to portfolio and reporting region
- ✓Sensitivity to hazard source, resolution, buffer, and aggregation choices
// Buyer questions
Frequently asked questions
Do you provide underwriting or actuarial advice?+
No. We engineer location quality, spatial exposure measures, and decision-support workflows; licensed risk professionals retain underwriting and actuarial judgment.
How does spatial analytics enhance flood risk analytics and wildfire catastrophe modeling for insurance?+
Rather than relying on generalized zone maps, our geospatial models integrate 1m–5m digital elevation models (DEMs), hydrologic flow accumulation, satellite-derived historical flood footprints, and vegetation fuel loads (dNBR/NDVI) to calculate property-level peril hazard scores and aggregate accumulation risk across coastal and wildland-urban interface (WUI) exposures.
Can you work with sensitive policy data?+
Yes. We support sanitized discovery samples, client-controlled environments, least-privilege access, encryption, retention rules, and auditable processing.
Can this support rapid event response?+
Yes. Versioned event footprints can be intersected with exposure locations to create a review list with confidence, source, and aggregation context.
How accurate is your address geocoding for catastrophe modeling?+
We build multi-tiered geocoding engines that match addresses down to the exact rooftop, parcel centroid, or street segment, providing explicit confidence scores and routing un-matched addresses to exceptions queues.
Can the platform model accumulation risk for non-linear perils like flood and wildfire?+
Yes. We intersect policy locations with high-resolution flood depth grids, wildfire burn probability layers, and earthquake soil amplification models to compute localized portfolio accumulation beyond arbitrary postcode boundaries.
What is the turnaround time for post-catastrophe event footprint analysis?+
Within 4 to 12 hours of satellite acquisition or meteorological data availability, we generate event footprints and intersect them with your exposed policy portfolio to produce initial loss estimates and claims inspection queues.
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Bring us the problem, not a perfect brief.
Three fields start the conversation. An engineer will help determine fit, data readiness, and the smallest useful next step.
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