Insurance
Use Ontologies to Accelerate Claims, Detect Fraud, and Ensure Compliance
The Problem
Fragmented Across Platforms
Policy, claims, geospatial, and customer data live in disconnected systems — Snowflake, Azure, GCP — making it impossible to connect them for risk pricing or fraud detection without expensive pipelines.
Slow & Brittle Analysis
PySpark and SQL workflows take weeks to update and break under new data models. Actuaries and analysts can't query relationships across claims, policy, and external data without engineering support.
Compliance & Invisible Risk
Fraud networks span policyholders, providers, and intermediaries in ways table-based analysis can't surface. Regulatory scrutiny demands explainable, audit-ready reasoning across every decision.
The Perfect Ontology Solution
Why It Matters Now
- Growing fraud sophistication with rings spanning policyholders, providers and intermediaries
- Regulatory scrutiny across Solvency II, GDPR, and consumer duty requirements
- Customer expectations for fast, fair claims and personalised service
- Underwriting accuracy demands connected views of risk across geospatial, policy and claims data
- AI explainability mandates require traceable, auditable decision pipelines
Solution & Features
- Unify Snowflake, Azure, GCP and on-premise data without moving it
- Connect geospatial, policy, and claims data into one queryable ontology
- Detect fraud networks across policyholders, providers, and intermediaries
- Trace cascading failures and simulate shock propagation scenarios
- Replace PySpark and SQL pipelines with declarative ontology logic
- Built-in governance with full data lineage and audit trails
- Combine structured and unstructured data for richer risk modelling
Use Cases
- Fraud network mapping across policyholders, agents and providers
- Natural-language claims data querying without engineering support
- Risk pricing with connected claims, telematics, and weather data
- Audit-ready regulatory reports (Solvency II, consumer duty)
- Personalised service through unified customer ontologies
- Shock propagation simulation for catastrophic event modelling
- Provider network analysis to detect billing anomalies
- Subrogation and recovery opportunity identification
Benefits
- Uncover fraud networks invisible to row-based analysis
- Reduce claims processing time with automated, governed workflows
- Price risk correctly with richer, connected data
- Meet audit requirements with full data lineage built in
- 10x faster data pipelines — no PySpark rewrites
- Empower non-technical users to query complex data directly
- No data migration — works on your existing infrastructure
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