Insurance Claims Approval with Fraud Escalation

The problem

Submitted claims are evaluated against coverage policy, fraud signals, and payout authority limits. Low-risk, policy-compliant claims auto-approve within seconds. High-risk claims escalate to investigators with full context preserved — not rejected silently. Adjusters retain decision authority while AI handles triage volume.

Without deterministic enforcement, AI agents either block every edge case (adding manual overhead) or silently approve decisions that violate policy — with no audit trail to show auditors or regulators.

How Corules solves it

Corules sits between your AI agent and the action it wants to take. When the agent proposes a decision, Corules evaluates the full context against your compiled policy set in a single deterministic pass — no LLM, no ambiguity.

The result is a structured outcome: ESCALATE — Fraud score 0.87 exceeds threshold 0.70. Escalating to fraud investigation with full claim context.

Policy example

Corules policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated deterministically at request time — no LLM, no variability.

// Claims policy (CEL)
context.claim_amount <= params.auto_approve_limit
  && context.fraud_score < params.fraud_threshold
  && context.coverage_verified == true
  && context.claim_type in params.covered_claim_types

This expression is evaluated against the structured context your agent sends in the /v1/validate request.

Integration options

Corules integrates with the tools your teams already use. All integrations call the same REST API or MCP server — your policy logic stays in one place.

Frequently Asked Questions