Fraud Controls
Part of Payment authentication and fraud controls
Testing fraud rules before applying them to every order
Testing fraud rules before applying them to every order: practical criteria and a clear evaluation process for marketing teams.
A fraud rule should be tested for two things: whether the payment integration handles its action correctly, and which real orders the rule is likely to affect. A sandbox test can answer the first question; historical analysis and a limited rollout help answer the second. Neither alone proves that future fraud will fall.
Specify the proposed behaviour
Write the rule's condition, action and purpose. Is it meant to request authentication, send a payment for review or block it? Identify the payment methods, regions and order types within scope.
Define what should happen when the underlying signal is missing or unreliable. Set an owner and a rollback trigger before enabling the rule.
Build test cases that should match and should not match. Include a legitimate-looking order near the threshold, a repeated attempt and an incomplete signal. Use the provider's test environment to check the response shown to the shopper, the order state and the fulfilment queue.
In the sandbox, exercise authentication, decline and fraud-error outcomes with the provider's test values, then verify that a configured Adyen risk rule triggers as expected. These verify technical paths, not production impact.
Sandbox testing vs. production impact assessment
- Sandbox testVerifies technical path: correct response to authentication, decline, or fraud-error; uses provider test values (Stripe, Adyen).
- Production impact assessmentEstimates real-world effect using historical data; identifies potentially blocked legitimate orders; requires careful monitoring for fraud disputes.
Estimate production reach
Where transaction data is available, review the proposed rule against historical transactions. Review enough matched transactions individually to understand who would have been blocked or challenged.
Historical data does not include a perfect label for every blocked transaction, and customer behaviour may change after rollout. Treat the result as a risk estimate.
If the platform allows a review-only or narrow segment rollout, start there. Compare affected acceptance, manual workload and customer contacts with a suitable baseline.
Fraud disputes may take longer to appear, so an immediate drop in approved orders should not be called a fraud success. Keep issuer declines separate from merchant risk blocks.
Key considerations in fraud rule evaluation
- Historical data used for impact estimation
- Yes – but not perfect labels for all blocked transactions
- Fraud disputes may take time to appear
- Do not interpret immediate drop in approvals as fraud success
- Issuer declines vs. merchant blocks
- Keep separate in analysis
Expand deliberately
After reviewing early results and later fraud outcomes, adjust the condition or action before applying it to more traffic. Record the version and activation time so later changes can be traced.
If legitimate customers are caught unexpectedly, pause or roll back while the cause is investigated. Avoid stacking several new rules at once, as it becomes difficult to tell which one caused a change.
The release decision should say what the rule is expected to catch, who may be turned away, and what evidence would justify changing it.
