Building RateGuard AI: An Agentic Guardrail Against Silent Insurance Pricing Errors

Description and use

RateGuard AI automatically verifies and compares insurance pricing models, detecting hidden errors and quantifying financial risk before deployment.

Problem

Insurance pricing changes can silently introduce incorrect premiums due to implementation drift, leading to financial loss and regulatory risk.

Outcome

RateGuard AI reduces release risk by detecting silent pricing defects, quantifying financial impact, and automating evidence generation, enabling faster, safer deployment decisions.

Workflow

• 1. Compile approved pricing intent and target implementation into IPIR.
• 2. Compare abstract syntax trees for semantic differences.
• 3. Trace differences through dependency graph.
• 4. Generate boundary and interaction test scenarios.
• 5. Calculate expected premiums with deterministic oracle.
• 6. Execute target calculation and reconcile traces.
• 7. Identify first divergent node.
• 8. Query synthetic portfolio to measure blast radius.
• 9. Propose remediation and revalidate if workflow allows.
• 10. Output release decision and evidence package.

Evidence

RateGuard is a vendor-neutral, agentic pricing‑assurance system that compares pricing sources, generates targeted tests, independently recalculates premiums, measures portfolio impact, proposes remediation, and returns a conservative release decision.

Source

RateGuard reduces this risk by checking both structure and behavior. It does not conclude that a release is safe merely because two source files look similar.

Source