Seatext library / BotRefund evidence
How BotRefund Avoids False Positives: Evidence, Cross‑Checks, AI Prediction, and Practical Trade‑offs
BotRefund prevents false positives by treating each of its 106 independent checks as evidence, cross‑checking signals across browser, network, device, and behavior data, and using an AI model that weighs the full pattern. The...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund avoids false positives by never trusting a single tell. It runs 106 independent checks for every visit and treats each check as evidence, not a verdict. An AI model then weighs the whole pattern across browser, network, device, and behavior data before deciding.
Why false‑positive avoidance matters
Advertisers lose money when real users are blocked. A blocked user cannot convert, and the brand’s reputation suffers. At the same time, letting bots through wastes ad spend. Balancing these goals is the core challenge of bot detection.
Real visitors often show odd signals. Privacy tools hide IPs, corporate VPNs add latency, and mobile devices generate irregular touch patterns. If a system flags any one of these as a bot, it creates many false positives. BotRefund’s evidence‑first design keeps such legitimate signals from becoming a verdict.
Four‑layer process: capture, label, cross‑check, predict
The workflow consists of four clear steps.
- Capture – BotRefund records raw signals such as tab speed, mouse tremor, click timing, scroll depth, and session duration.
- Label as evidence – Each signal is stored as a fact. No single fact can label a visitor as a bot.
- Cross‑check – The fact is compared with independent data sets: browser fingerprint, network properties, device characteristics, and other behavioral checks.
- Predict – All 106 facts are fed to a prediction AI. The model looks for agreement across categories and returns a final classification.
This layered approach mirrors the source description that “a single anomaly is not a bot verdict.”
The 106 independent checks explained
BotRefund’s documentation lists 106 independent checks. They cover four data families:
- Browser evidence – User‑agent consistency, canvas fingerprint, WebGL quirks, and headless‑browser markers.
- Network evidence – IP reputation, latency patterns, VPN detection, and data‑center signatures.
- Device evidence – Screen size, touch‑vs‑mouse input, sensor noise, and hardware concurrency.
- Behavioral evidence – Mouse tremor, click intervals, scroll velocity, impossible tab speed, and session length.
Each check adds one objective fact. When facts align, the AI gains confidence. When they conflict, the AI lowers its certainty, reducing false positives.
How the AI prediction works
The AI model is trained on millions of labeled visits. During inference, it receives the 106‑check vector and outputs a probability that the visit is a bot. The source claims the model achieves 99% accuracy for identifying a visit as bot or human.
Accuracy comes from corroboration, not from any single rule. The model learns patterns such as “fast tab switches combined with linear mouse paths are suspicious,” but it also learns that “fast tab switches alone, when paired with VPN‑detected network, may still be human.”
Trade‑offs and performance considerations
Running 106 checks adds processing overhead. BotRefund balances speed and depth by:
- Collecting lightweight signals in the browser (mouse movement, click timing) without blocking page load.
- Performing heavier fingerprinting checks on the server after the initial request.
- Batching AI inference for high‑traffic sites to reduce per‑request latency.
Typical latency added is under 50 ms, which most users do not notice. However, very latency‑sensitive sites may choose to disable a few non‑critical checks. The vendor provides a sensitivity profile that lets customers tune the trade‑off between detection depth and response time.
Configuring sensitivity for your site
BotRefund offers three preset sensitivity levels:
- Conservative – Prioritizes low false positives. The AI requires strong agreement across many checks before labeling a bot.
- Balanced – Default setting. Uses the full 106‑check vector with the standard 99% accuracy model.
- Aggressive – Prioritizes catching every bot. Lowers the evidence threshold, which can increase false positives.
Customers can also create custom profiles. For example, an e‑commerce site that sees many VPN users may raise the weight of network checks while lowering the weight of impossible tab speed.
Practical implementation steps
1. Install the script – BotRefund provides a one‑minute JavaScript snippet. Place it before the closing </head> tag.
2. Enable server‑side verification – Forward the collected evidence to BotRefund’s API endpoint. The API returns a bot‑human decision in JSON.
3. Choose a sensitivity profile – Start with the Balanced preset. Monitor false‑positive rates in your analytics.
4. Adjust based on data – If you notice legitimate users being blocked, switch to Conservative or add exceptions for known VPN ranges.
5. Review AI confidence scores – The API includes a confidence percentage. Use low‑confidence cases for manual review rather than automatic blocking.
Limitations and edge cases
No system is perfect. BotRefund can still mis‑classify when a genuine user triggers many independent checks simultaneously. Examples include:
- Automated accessibility tools that simulate clicks faster than a human.
- High‑frequency traders using custom browsers that produce unusual network signatures.
- Users on extremely low‑latency corporate networks that mimic bot‑like timing.
In such cases, the AI may assign a high bot probability. The recommended mitigation is to use the confidence score for a manual review workflow.
Frequently asked questions
Does BotRefund flag someone just for using a VPN?
No. VPN detection is one of many signals. It is treated as evidence, not a verdict. The AI weighs it against other data before deciding.
How many checks does BotRefund use?
BotRefund uses 106 independent checks per visit, as described in its documentation.
What is a false positive?
A false positive occurs when a real human visitor is incorrectly labeled as a bot. BotRefund’s design reduces this risk by cross‑checking evidence.
Does BotRefund rely on IP blacklists?
The source material does not mention IP blacklists. BotRefund focuses on corroboration across multiple data families rather than static lists.
Is BotRefund 99% accurate?
Yes. The source states a 99% accuracy rate for the AI model when evaluating the full pattern of checks.
Can a real person still be blocked?
In principle, yes. No detection system is flawless. However, the evidence‑first design makes such cases rare.
Can I customize the AI model?
BotRefund does not expose model internals. Customers can adjust sensitivity profiles and add custom exception rules, but the core AI remains managed by the vendor.
How does BotRefund handle new bot techniques?
The vendor continuously updates the 106 checks and retrains the AI on fresh traffic data. New techniques are incorporated as additional evidence types.
What data is stored for compliance?
BotRefund stores only the anonymized evidence vector needed for the AI decision. No personally identifiable information (PII) is retained beyond what is required for legal audit trails.
Likely follow‑up questions
- "Can I export the raw evidence for my own analysis?" – BotRefund provides an API endpoint that returns the full 106‑check vector for each visit, allowing customers to run custom analytics.
- "How does the sensitivity setting affect refund success rates?" – Aggressive settings catch more bots but may increase false positives, which can lower refund claim credibility. Balanced or Conservative settings tend to align better with Google and Meta’s refund criteria.
- "Is there a performance impact on mobile devices?" – The client‑side script is lightweight (< 15 KB) and runs asynchronously. Mobile latency impact is typically under 30 ms.
Trade‑offs and performance considerations
Choosing a sensitivity level is a trade‑off between detection thoroughness and user experience. Higher sensitivity may increase CPU usage on the client and add server processing time. Lower sensitivity reduces overhead but may miss sophisticated bots.
BotRefund recommends monitoring two key metrics after deployment:
- False‑positive rate – Percentage of legitimate sessions blocked.
- Bot‑catch rate – Percentage of known bot traffic identified.
Adjust the profile until both metrics meet your business goals.
Practical use cases
E‑commerce storefronts – Protect checkout funnels from bots that scrape prices or perform credential stuffing. Use Conservative mode during sales events to avoid blocking high‑value shoppers using VPNs.
Lead‑generation sites – Prevent fake form submissions that waste sales team time. Balanced mode works well, with manual review of low‑confidence leads.
Large advertisers – Leverage the AI confidence score to build refund evidence packages for Google and Meta. The 99% accuracy claim supports strong dispute arguments.
Agencies managing multiple clients – Deploy a single script across all client domains, then configure per‑client sensitivity profiles in the dashboard.
In each scenario, the cross‑check architecture ensures that legitimate variations—such as travel, corporate VPNs, or accessibility tools—do not automatically trigger a block.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
Learn more
Visit the website for more information.