Underwriting-Grade Risk Intelligence from Orbit

Climate volatility has made historical actuarial data an increasingly unreliable predictor of future losses. Traditional catastrophe models, built on decades of historical data, struggle to account for the rapid pace of environmental change — flash floods in previously arid regions, subsidence in areas with no history of ground movement, wildfire spread patterns that defy historical precedent. Insurers and reinsurers pricing risk today are working with models that may not reflect tomorrow's reality.

The gap between when a loss event occurs and when actionable intelligence is available is often measured in days or weeks, not minutes or hours. Post-event claims triage relies on physical inspections, aerial surveys commissioned after the fact, and manual damage assessment. This latency increases loss adjustment expenses, delays claimant communication, and creates operational bottlenecks during catastrophe events when volume spikes.

Portfolio-level stress testing requires understanding correlated risks across geographic regions — a challenge when risk data is fragmented across multiple vendors, inconsistent in methodology, and lacks the provenance and auditability required for regulatory reporting. Insurers need a unified, measurement-grade intelligence layer that provides confidence-scored risk assessments at both individual-location and portfolio scales.