Catastrophe Modeling Reference Architecture for Insurance
This architecture demonstrates how insurers can integrate geospatial, weather and claims data to predict losses and reduce exposure.
Data flows
The following are descriptions of the data flows shown in the catastrophe modeling architecture diagram:
- Ingest both structured and unstructured geospatial data (e.g., LiDAR, raster, vector formats) from diverse sources.
- Use Databricks Auto Loader to incrementally ingest data into Delta Lake, landing in the Bronze layer (raw) of the medallion architecture (Bronze, Silver, Gold).
- Build structured streaming pipelines to continuously process geospatial data across medallion layers. Apply data quality checks, schema enforcement and geospatial business rules (e.g., proximity filters, spatial joins). Leverage Databricks-supported spatial libraries such as Mosaic for efficient spatial processing and indexing.
- Use Databricks SQL to run optimized spatial queries over Delta Lake (e.g., H3 cell aggregations, point-in-polygon, line-of-sight analysis). Develop dashboards and enable natural language querying to empower business users with self-serve analytics. Optionally integrate with visualization tools like Esri and CARTO via Partner Connect for advanced geospatial analytics and interactive mapping.
- Apply machine learning for forecasting and predictive spatial modeling (e.g., land use change, traffic flow). Use MLflow for experiment tracking and model management.
- Present results via Databricks Apps with interactive geospatial visualizations for stakeholders.
Benefits
Benefits of using the Databricks Platform for call center architecture include the following:
- Establish best-practices architecture for catastrophe modeling use cases
- Learn about AI solutions on geospatial data relevant to catastrophe modeling and how they differentiate Databricks as the industry leader
