Infrastructure for Catastrophe Claims Surge Management

Catastrophe surge management requires documented protocols for adjuster pre-positioning thresholds, vendor activation criteria, reinsurance trigger assessment, and reserve establishment guidelines by event type. These must be current and findable so the AI applies consistent deployment recommendations when a hurricane or wildfire event occurs. Cat response plans that exist only in experienced CAT team managers' memory cannot be encoded into a surge prediction and resource optimization model.

Catastrophe surge prediction requires systematic capture of historical cat event data—claim frequency and severity by ZIP code, event intensity metrics, adjuster deployment outcomes, and vendor performance records—through defined post-event documentation processes. Template-required capture after each CAT event builds the training dataset for frequency-severity models. Without systematic post-event capture, the predictive model is trained on sparse, inconsistently formatted historical data.

Catastrophe surge prediction requires consistent schema linking event characteristics (event type, intensity, affected geography) to historical claim outcomes (frequency per exposed policy, average severity, adjuster hours per claim) and resource capacity data (adjuster locations, vendor coverage zones). Consistent schema across cat events enables the model to compute deployment recommendations. Cross-event ontology is not required at this level—consistent field definitions within each event type are sufficient.

Catastrophe surge management must query policy-in-force data by location (to compute exposed policy count), weather event feeds (intensity and affected area), historical cat claim data, and adjuster/vendor capacity systems via API at the time of event onset. Real-time policy location data is critical—batch extracts from the prior day miss policies bound in the 24 hours before a storm makes landfall. API access to the policy admin and cat modeling platforms enables current exposure computation.

Catastrophe surge models must incorporate near-real-time updates from weather feeds, claim development data as the event unfolds, and vendor capacity changes as contractors mobilise or exhaust availability. When NOAA updates a hurricane track or intensity forecast every 6 hours, the surge model must re-compute deployment recommendations immediately. Stale weather inputs during an active event generate adjuster deployment decisions that are wrong by the time teams are in position.

Catastrophe surge management requires API integration between the policy admin system (exposure data), weather data providers (event path and intensity), claims system (incoming FNOL volume), adjuster management platform (capacity and location), vendor management system (contractor availability), and reinsurance reporting. These connections must be active at event onset so the AI can compute deployment recommendations from current exposure, event parameters, and resource availability simultaneously.