AI Underwriting
Models assess the evidence. Deterministic rules make the decision.
Medical, financial and fraud risk are scored in parallel by a LangGraph workflow, combining LLM analysis with hard floors and ceilings. A separate rule-based decision engine then picks one of seven outcomes, so every verdict can be explained and replayed.
Underwriting case
CASE-2026-A3F9C1
Ahmed Khan · Term Life 20 · PKR 5,000,000
Verdict
Approve with Loading
- Requirements satisfied
- Scored in parallel
- Rule chain evaluated
Capabilities
What AI Underwriting covers
Requirements engine first
Mandatory evidence is derived from age, cover, cover-to-income, smoker status, BMI and declared conditions. Until it is satisfied, the risk engine is never called.
Document intelligence
OCR and visual analysis extract and verify uploaded documents, and a summariser produces underwriter notes.
Seven explicit verdicts
Auto Approve, Approve with Loading, Human Review, Request Additional Evidence, Fraud Investigation, Postpone, or Decline.
Streaming evaluation
Watch each node of the workflow complete live, or run it asynchronously over Kafka for bulk jobs.
Guarantees
Built to be trusted
- Final branching is rule-based, not model-generated
- Primary and fallback LLM provider configurable at runtime
- Every evaluation is persisted and auditable
See the full journey on demo data
We will walk you through a lead becoming a policy and a claim, with the copilot driving where it helps.