AI Refund Authorization with Anti-Fraud Coruless

The problem

Refund requests evaluated before processing. Per-customer monthly limits, reason code validation, and repeat-refunder detection prevent abuse while legitimate customers receive fast resolution. Each refund carries an audit trail linking the agent, reason, and policy version — defensible in chargeback disputes.

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 — Customer has 4 refunds in last 30 days (limit: 3). Escalating to customer fraud team.

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.

// Refund policy (CEL)
context.refund_amount <= params.max_refund_per_request
  && context.customer_refund_count_30d < params.max_refunds_per_customer_30d
  && context.refund_reason in params.valid_refund_reasons
  && context.order_age_days <= params.max_refund_window_days

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