Seatext library / BotRefund evidence
How to Test BotRefund Integration with Your Existing Fraud Setup
To test BotRefund safely, use the sandbox environment to simulate fraud scores and webhook deliveries. Validate your end-to-end refund flows by triggering test payloads that mimic bot behavior without impacting real financial data.
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Testing Your BotRefund Integration
Integrating BotRefund with your current fraud stack requires a controlled validation phase. By using the sandbox environment, you can verify that BotRefund correctly identifies behavioral anomalies—such as superhuman input speeds or robotic mouse movements—and passes that data to your existing systems without affecting live payouts.
| Test Phase | Action | Expected Outcome |
|---|---|---|
| Environment Setup | Deploy the tracking script in a staging environment. | Script initializes and captures session data. |
| Payload Simulation | Inject test bot signals (e.g., sub-1ms inputs). | BotRefund flags session as 'Reject' or 'Hold'. |
| Webhook Validation | Trigger a test event to your CRM or fraud engine. | System receives the JSON payload correctly. |
| End-to-End Flow | Simulate a full conversion path. | Evidence dashboard populates with audit data. |
Interpreting BotRefund Tags: Approve, Review, Hold, Reject
BotRefund scores every affiliate conversion and assigns one of four tags. Understanding what each tag means is critical for your test plan.
Approve means the session looks clean. The buyer behaved normally, and the attribution path is intact. Your finance team can pay this commission without extra checks.
Review means some anomalies appeared. It might be a slightly unusual click-to-conversion time or a minor pointer pattern deviation. You should look at the evidence before deciding.
Hold means strong fraud signals are present. Payout should pause until you investigate. Examples include a superhuman input speed or a session with no scrolling.
Reject means clear evidence of manipulation exists. The commission should be declined. Cookie stuffing or last-click hijacking often produce this tag.
In your tests, map each tag to a specific action in your fraud workflow. For example, 'Hold' might trigger a manual review queue. 'Reject' could auto-decline in your payout system.
Simulating Common Fraud Types in the Sandbox
Your test plan must include realistic fraud simulations. Focus on the three patterns BotRefund is built to catch.
Cookie Stuffing
Cookie stuffing places a tracking cookie without user interaction. To simulate it, inject a cookie via a hidden iframe on a test page. Then complete a normal purchase. BotRefund should flag the session because the cookie appeared without a visible click.
Coupon Overwrites
Browser extensions often overwrite affiliate cookies at checkout. Simulate this by programmatically changing the affiliate cookie value right before the conversion event. Check that BotRefund's attribution path analysis detects the change and tags it 'Review' or 'Reject'.
Last-Click Hijacking
Last-click hijacking happens when an affiliate redirects or drops a cookie in the final seconds. In the sandbox, create a user journey where you visit an affiliate link, then switch to a direct visit just before conversion. BotRefund should note the late attribution change.
For each simulation, use the same behavioral patterns you expect from real bots—straight mouse paths, sub-1ms inputs, and no page scrolling. This ensures your test data matches production threats.
Validating Webhook Payloads and Schema
BotRefund sends webhooks with scoring data to your endpoint. You must verify the payload structure before trusting it.
Start by reviewing the documented JSON schema. The payload typically includes fields like session ID, click ID, conversion ID, tag, score, behavioral signals, and attribution path details.
Write a simple validator that checks for required fields and data types. For example, ensure the 'tag' field is one of the four allowed values. Validate that the 'timestamp' is in ISO format and the 'score' is a number.
Test error handling. What happens if your endpoint returns a 500? BotRefund should retry with backoff. Confirm your system can process duplicate events without double-counting.
Also test the webhook under load. Sending 100 test events in one second should not drop any payloads. Your fraud engine must keep up with peak traffic.
Handling False Positives and Edge Cases
No fraud tool is perfect. False positives—legitimate customers flagged as bots—are inevitable. Your test plan must address them.
Create a set of 'clean' test sessions with real human behavior. Use a real mouse, move with natural curves, scroll randomly, and pause between actions. Run these through BotRefund and confirm most get 'Approve' tags.
Edge cases matter. Some users are power clickers. Others use trackpads that produce straight movements. Seasonal traffic may behave differently. Test those variations.
When a false positive appears, check the evidence dashboard. Look at the specific signal that triggered the flag. If it was 'grid-aligned movement', the user might have been using a graphic tablet. You can whitelist certain device profiles or adjust your workflow.
Plan a manual review queue for 'Review' and 'Hold' tags. This gives your team a buffer between bot detection and payout decisions. It also reduces the risk of rejecting a real customer.
Running a Parallel Audit Before Switching
The safest way to test BotRefund is to run it in audit mode alongside your current fraud system. Do not switch immediately.
- Install BotRefund's tracking script on your staging or production site without activating webhooks.
- Let it collect data for a full payout cycle—typically 30 days.
- Compare BotRefund's tags against your existing fraud tool's decisions.
- Investigate every disagreement. Does BotRefund flag something your current tool missed? Is it a false positive?
- Calculate the potential savings from commissions you would have paid versus legitimate customers BotRefund might block.
This parallel run gives you confidence. It also builds evidence for your finance team. They see real data before approving a full rollout.
Building a Repeatable Test Plan
A good test plan is structured and repeatable. It lets you verify new changes quickly.
Create a checklist with these steps:
- Reset the sandbox data to a clean state.
- Run a baseline clean session and confirm 'Approve'.
- Simulate one fraud pattern and confirm the expected tag.
- Test webhook delivery to your endpoint using a test event.
- Check the evidence dashboard for complete session data.
- Verify that your internal workflow acts on the tag correctly.
- Log each test with a timestamp and expected vs actual outcome.
Automate what you can. Use a small script to generate test sessions with known parameters. This allows continuous integration testing whenever BotRefund releases updates.
Understanding the Evidence Dashboard
The evidence dashboard is your proof. It shows every session with its behavioral signals, device data, and attribution path.
When you examine a session, look for the specific signals BotRefund uses: ghost clicks, honeypot interactions, linear mouse movements, missing tremor, superhuman input speed, grid-aligned paths, lack of scrolling, and unusual session lengths.
The dashboard also visualizes the attribution path. You see which affiliate ID and click ID were present at each step. This is crucial for spotting cookie stuffing or last-click hijacking.
For a rejected commission, export the evidence as a PDF or CSV. This becomes the documentation you send to your affiliate manager or use in a payout dispute.
Trade-Offs and Limitations to Consider
BotRefund adds a JavaScript tag to your site. This can slow page load slightly. Measure the impact during your test.
It also depends on JavaScript being enabled. If a bot uses a headless browser with scripts disabled, BotRefund may not capture data. However, most modern bots execute JavaScript to mimic humans.
BotRefund works best with full session data. If a user clears their cookies mid-session, the attribution path may break. Your tests should include cookie resets.
Remember that BotRefund is a detection layer, not a replacement for a comprehensive fraud strategy. Use it alongside your existing tool to cover different threats.
Practical Tips for a Smooth Testing Process
- Use separate tracking IDs for sandbox and production to avoid data mixing.
- Start with low-volume tests before scaling up to stress tests.
- Involve your finance team early. They need to trust the evidence.
- Document every test scenario so you can replicate it later.
- Check with the vendor for the latest webhook schema updates.
Testing BotRefund properly takes a few hours but saves months of payout mistakes. A structured approach gives you confidence before go-live.
Frequently Asked Questions
Can I test BotRefund without changing my current fraud setup?
Yes. BotRefund is designed to work alongside your existing tools. You can start by running it in 'audit mode' to compare its findings against your current fraud detection results.
Does testing require real money?
No. You should perform all integration tests using simulated traffic in a sandbox environment to ensure your logic is sound before moving to production.
How do I know if the integration is working?
Check your Evidence Dashboard. If you see session data, behavioral tags, and attribution paths for your test traffic, the integration is successfully capturing the required signals.
What if my current fraud setup is already blocking bots?
BotRefund provides a secondary layer of protection by analyzing the attribution path and post-click behavior, which are often missed by standard click-level fraud tools.
How long should the parallel audit run?
Run it for at least one full payout cycle—typically 30 days. This covers different traffic patterns and gives you enough data to compare.
Can BotRefund detect all types of affiliate fraud?
No. It focuses on behavioral signals and attribution path manipulation. It may miss fraud that occurs entirely off-site or through manual non-automated methods.
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Further reading and comparison sources
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