AI Policy Enforcement for Insurance Claims and Underwriting

Industry context

Insurance carriers use Corules to gate claims adjudication and underwriting decisions. Legitimate claims auto-approve within seconds. Fraud signals trigger escalation with full context preserved for investigators — not silent rejection. Every decision is logged for state insurance department examination.

Corules provides the deterministic policy enforcement layer that Insurance organizations need to deploy AI agents in production — with audit trails that satisfy regulators and governance teams.

Regulatory requirements

State insurance codes require prompt payment of covered claims (typically 30 days from notice). Claims denials require specific written reasons. Adverse underwriting decisions (non-renewal, cancellation) require state-mandated notice periods and reason codes. AI use in claims and underwriting is increasingly regulated at state level.

Corules's immutable audit ledger records every decision with: policy set version, actor identity (from signed claims — never from LLM output), normalized input hash, outcome, and reason. This creates a complete compliance trail for any regulatory examination.

Key decision types

These are the structured decisions most commonly enforced with Corules in Insurance:

• First notice of loss triage and severity classification
• Claims approval within adjuster authority limits
• Fraud signal escalation to SIU
• Policy coverage verification and exclusion application
• Subrogation opportunity identification

Applicable use cases

These Corules use cases are commonly deployed in Insurance organizations:

Frequently Asked Questions