Insurance Risk Assessment Using Geospatial Constellation Data Workflow

Automatically scoring properties against high-resolution flood extent, building footprint, and vegetation density data reduces reliance on outdated postal code-level models. This enables more precise risk differentiation, allowing insurers to price more competitively for low-risk properties and avoid underpricing high-risk exposures. The result is a direct improvement in combined ratio through better-segmented portfolios.

Upon a flood or wildfire event, the workflow automatically ingests post-event satellite imagery, compares it to pre-event baselines, and generates a geofenced damage assessment report. This allows claims teams to triage thousands of properties in hours—not weeks—prioritizing the most severely affected policies and deploying adjusters efficiently. It turns a reactive, chaotic process into a data-driven operational response.

Eliminates the need for underwriters and claims analysts to manually download, clip, and interpret satellite data layers from disparate providers like Planet, Sentinel Hub, or commercial SAR operators. The workflow normalizes data feeds, runs pre-configured risk models, and surfaces only actionable insights within core systems like Guidewire, Duck Creek, or Salesforce. This reclaims hundreds of hours of specialist labor per quarter.

Provides a continuous, automated feed of geospatial risk scores across the entire property book. Risk managers can monitor aggregate exposure to perils like coastal erosion or wildfire encroachment in near-real-time, enabling proactive reinsurance purchasing or portfolio rebalancing decisions. This moves exposure management from a quarterly spreadsheet exercise to a dynamic, always-on control layer.

Automates the creation of an immutable audit trail for every risk decision influenced by satellite data. The workflow logs the specific data sources, model versions, inference timestamps, and scoring logic applied to each property. This provides a defensible, reproducible basis for pricing and underwriting decisions during regulatory reviews or litigation, reducing compliance risk.

Enables the rapid creation and pricing of insurance products in previously unserved or data-poor regions (e.g., parametric flood insurance in emerging markets). By automating the ingestion and risk modeling of globally consistent satellite data, insurers can de-risk entry into new territories without establishing costly local inspection networks, accelerating time-to-market for innovative covers.

This component automates the continuous ingestion of raw data feeds from commercial satellite constellations (e.g., Planet, Sentinel) and aerial sources. It handles API polling, formats disparate data types (optical, SAR, multispectral), and normalizes geospatial coordinates and timestamps into a unified data lake. The automation eliminates manual data procurement and preparation, which typically consumes 15-20 hours per analyst weekly, ensuring underwriters and claims adjusters have a consistent, current data foundation.

This is the core AI processing layer where specialized computer vision and geospatial models run in orchestrated pipelines. Key models include:

• Flood Extent Segmentation: Uses SAR data to map water bodies and detect inundation.
• Building Footprint & Roof Condition: Extracts structures and classifies roof material/condition from high-res imagery.
• Vegetation Density & Fuel Load Analysis: Calculates NDVI and other indices to assess wildfire fuel proximity and health.
• Change Detection: Identifies new constructions, demolitions, or landscape alterations between time-series images. The workflow automatically routes imagery to the appropriate models, runs batch or on-demand inference, and stores vectorized outputs (e.g., GeoJSON) for scoring.

This agentic component applies business logic to the extracted geospatial features. It ingests property addresses, parses location-specific underwriting guidelines, and calculates a composite risk score. For example, it can automatically flag properties within a 100-year floodplain, within 1 mile of high-fuel vegetation, or with roofs older than 15 years. The orchestrator integrates with rules engines (e.g., IBM ODM, custom) and can trigger immediate alerts for high-risk submissions or portfolio exposures, replacing manual map analysis and spreadsheet scoring.

The automation is worthless without integration. This component manages bidirectional APIs with:

• Policy Administration Systems (e.g., Guidewire, Duck Creek): Pushes risk scores and geospatial evidence into underwriting workbenches at submission or renewal.
• Claims Management Systems (e.g., ClaimCenter): Automatically attaches pre-event and post-event imagery to claims files for validation, detecting potential misrepresentation.
• CRM & Agent Portals: Surfaces risk insights to agents for pre-qualification discussions. The router also manages exception handling, escalating cases with low model confidence or conflicting signals to human underwriters via ticketing systems like ServiceNow.

A non-negotiable control layer for regulated insurance operations. This component provides:

• Explainability Logs: Stores the specific geospatial features and rules that contributed to each risk score, creating an audit trail for regulators and appeals.
• Performance Drift Monitoring: Tracks model accuracy (e.g., flood prediction vs. actual claims) and triggers retraining pipelines when performance degrades.
• Human-in-the-Loop Gates: Configurable approval steps where high-value policies or complex claims require a human to review the AI's evidence and score before binding or paying. This ensures the workflow remains accurate, defensible, and aligned with evolving underwriting appetites.

A practical build requires phased deployment to manage risk and demonstrate ROI. A typical sequence includes:

1. Pilot: Integrate flood model scoring for new submissions in a single high-risk territory (e.g., Florida), running in parallel with manual processes for validation.
2. Scale: Expand to all perils (wildfire, hail) and geographies for new business, integrating fully with the PAS.
3. Portfolio Analysis: Run the workflow retrospectively on the entire in-force book to identify concentration risk and model exposure.
4. Claims Validation: Extend the pipeline to the claims department, automating the attachment of pre/post-event imagery for specific catastrophe codes. Each phase requires clear success metrics (e.g., reduction in loss ratio for scored policies, claims fraud detection rate) and stakeholder training.

Automates the continuous ingestion of raw satellite feeds (optical, SAR, multispectral) from providers like Planet, Sentinel Hub, or Maxar. The workflow normalizes disparate data formats, applies cloud masking, and performs initial orthorectification, creating a clean, analysis-ready data layer. This eliminates manual data wrangling and ensures consistent input for downstream risk models.

Orchestrates specialized computer vision and geospatial analytics agents to extract risk-relevant features from the normalized imagery. This includes:

• Flood Extent Mapping using water index algorithms.
• Building Footprint & Roof Condition segmentation.
• Vegetation Density & Wildfire Fuel Load analysis.
• Proximity to Hazards (e.g., coastlines, fault lines). Agents output structured geojsons and scores, replacing manual GIS analysis.

Connects the geospatial risk layer to the system of record. For underwriting, the workflow pushes risk scores and evidence (e.g., flood zone overrides) into the Policy Administration System (PAS) like Guidewire or Duck Creek to pre-fill applications and influence rating. For claims, it automatically retrieves the policy and loss location, runs post-event analysis against a pre-event baseline, and attaches validation evidence (e.g., proof of damage, pre-existing condition) to the claim file in systems like ClaimCenter or Salesforce.

Integrates with actuarial and exposure management platforms (e.g., RMS, AIR). The workflow automatically geocodes the entire policy portfolio, enriches each location with current satellite-derived risk attributes, and feeds this updated exposure set into cat models for real-time loss simulations. This moves exposure management from quarterly batch updates to a continuous process, dramatically improving the accuracy of aggregate risk and capital allocation decisions.

Embeds governance by routing low-confidence model outputs, high-value exceptions, and escalation triggers to the appropriate roles via integrated ticketing (ServiceNow, Jira) or dedicated underwriting/claims workbenches. For example, a property flagged with potential unpermitted structures is queued for an underwriter review with imagery and extracted features pre-attached. This ensures auditability and expert oversight where needed.

Integrates with MLOps platforms (MLflow, Weights & Biases) and observability stacks (Datadog, Grafana) to track data drift in satellite inputs, monitor feature extraction model performance, and log all automated decisions for audit. The workflow automatically triggers model retraining pipelines when performance degrades and can A/B test new models on a subset of traffic, ensuring the risk assessment system remains accurate as environmental patterns change.