Property Insurance: Multi-Agent Automation for Wildfire or Flood Peril Assessment

Manual verification of a reported loss against wildfire perimeters or flood maps can take adjusters hours per claim, creating a massive backlog during large-scale events. A custom workflow ingests claim location and timestamp, then uses agentic orchestration to query satellite imagery APIs (e.g., Sentinel Hub), government event databases (e.g., USGS, FEMA), and hyperlocal weather feeds in parallel. It generates a peril verification report in under 60 seconds, allowing triage teams to prioritize thousands of claims based on objective, real-time data instead of manual lookups.

A significant portion of property claim leakage comes from fraudulent submissions for losses that did not occur within a documented catastrophic event. This workflow automates an objective first line of defense. An analysis agent cross-references the reported incident date and geocoordinates against authoritative fire progression maps or flood inundation models. Claims falling outside verified event parameters are automatically flagged and routed to a Special Investigations Unit (SIU) queue with a compiled evidence package, strengthening the insurer's position and reducing loss adjustment expense (LAE).

Not all properties within a wildfire zone experience equal damage. Manual assessment lacks granularity. This workflow deploys a severity-scoring agent that analyzes proximity to the fire's hottest intensity contours (derived from satellite thermal data) or, for floods, water depth estimates from hydraulic models. It generates a preliminary severity band (e.g., Low, Medium, High, Total Loss Likelihood) and attaches it to the claim file. This allows adjusters and third-party administrators (TPAs) to immediately dispatch field resources to the most severely affected properties, optimizing inspection logistics and improving initial reserve accuracy.

For claims verified within an event area but scored with low probable severity, the workflow can trigger a straight-through processing lane. This involves automated customer communication to request self-service photo documentation, integration with AI-powered property damage estimation tools, and direct routing to approved contractors for rapid repair—all without adjuster assignment. This architecture, built with frameworks like LangGraph for agent coordination, can handle 30-40% of CAT claims automatically, dramatically reducing per-claim handling cost and freeing expert staff for complex cases.

The workflow acts as a structured data pipeline, transforming raw geospatial and weather signals into auditable, claim-level peril attributes. By aggregating this data across an event, insurers gain a precise view of their exposure footprint and loss progression. This enriched dataset feeds directly into reinsurance reporting and capital modeling platforms, providing actuarial and risk teams with higher-fidelity data for trigger validation, portfolio optimization, and real-time capital adequacy assessments during a developing catastrophe.

A viable build starts with a 6-week pilot focusing on a single peril (e.g., wildfire) in a high-exposure region. The architecture centers on an orchestration layer (LangGraph) managing three core agents: a Data Ingestion Agent (APIs for NOAA, NASA FIRMS, CalFire), a Geospatial Correlation Agent (PostGIS/GeoPandas logic), and a Reporting & Routing Agent. It integrates with the core claims system (e.g., Guidewire) via a secure API gateway. Critical controls include human-in-the-loop approval for high-severity flags, immutable audit logs of all data sources used, and a fallback to manual process for any agent confidence score below a set threshold.

This agent ingests real-time feeds from government wildfire perimeters (e.g., NIFC), flood inundation maps (e.g., FEMA), and hyperlocal weather APIs. It performs a spatial join to verify if a reported property location falls within a documented catastrophic event area and time window. The output is a binary verification flag and a confidence score, eliminating manual checks against static maps and preventing fraudulent claims for non-events.

For verified events, this agent calculates the property's proximity to the event epicenter (e.g., fire front, river gauge). It ingests high-resolution satellite or drone imagery (Sentinel-2, Planet) and applies pre-trained models to assess visible indicators like burn scar intensity or water depth. The agent generates a severity tier (e.g., low, moderate, high) and a preliminary exposure report, enabling adjusters to prioritize the most severe claims immediately after a CAT event.

This orchestrator agent compiles the outputs from the verification and analysis agents into a structured, auditable Peril Verification Report. It formats geospatial evidence, timestamps, data sources, and confidence metrics into a PDF or JSON payload compatible with the claims core system (e.g., Guidewire ClaimCenter). The report is automatically attached to the claim file, providing adjusters with a ready-made evidence package that supports coverage decisions and reduces manual documentation by 60-70%.

The workflow's control plane manages integrations with core policy systems for location data, ingests imagery via cloud storage or direct APIs, and handles exceptions. Claims failing verification or with low-confidence analyses are automatically routed to a human-in-the-loop queue for adjuster review, with all context preserved. This layer, often built with LangGraph or Temporal, ensures reliable handoffs, maintains audit trails, and allows for phased rollout by peril type or region.

A background monitoring agent subscribes to catastrophe modeling alerts and third-party event feeds. Upon detecting a new qualifying wildfire or flood event, it automatically activates the peril assessment workflow for all properties within the affected ZIP codes or geofence in the insurer's portfolio. This proactive trigger allows the claims operation to begin triage and customer communication before the first FNOL call is received, shifting response from reactive to proactive.

Critical for production reliability, this component continuously evaluates the accuracy of geospatial models, tracks data source uptime, and monitors for drift in severity classifications. It generates alerts for operational teams and routes low-confidence assessments for human review. By embedding this governance layer, the workflow maintains regulatory and internal compliance, ensures the audit trail is defensible, and creates a feedback loop to retrain computer vision models based on adjuster overrides.