The definitive guide to the commercial P&C underwriting landscape: Strategy, evolution, and the AI imperative

Traditional OCR and rules-based tools struggle with real-world variability: changing formats, embedded tables, and narrative context. LLMs act as document engineers that understand both structure and meaning, enabling normalization, anomaly detection, and structured output that rules engines cannot produce at scale.

CURE is not a point solution. It is a unified architecture that separates data, analytics, and workflow layers so that inbound submissions flow through AI-enabled ingestion and normalization into a governed underwriting data store, giving every underwriter a single account view instead of a patchwork of systems.

It means maintaining model inventories with regular validation and back-testing, generating both global and local explainability outputs, running active bias and fairness assessments, and creating cross-functional committees where CUOs, compliance, and front-line underwriters review AI behavior with real examples from production.