Seatext library / BotRefund evidence
Bot Detection Accuracy for Google Ads: How Multi-Signal Verification Works
Bot detection accuracy for Google Ads relies on corroborating dozens of independent signals — behavioral, network, and biometric — rather than any single rule. BotRefund uses 106 checks cross-referenced by an AI model to...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Bot detection accuracy for Google Ads is not a single metric. It depends on how many independent signals a system cross-checks before labeling a click as invalid. BotRefund runs 106 separate checks — covering click behavior, pointer dynamics, network fingerprints, and biometric timing — and feeds them into an AI prediction layer that weighs the full pattern. The company states this corroboration approach yields 99% accuracy and that 83% of its customers successfully recover refunds from Google and Meta, with claims dating back to 2017.
How bot detection accuracy works for Google Ads
Accuracy comes from evidence stacking. A single anomaly — a fast click, a straight mouse line, a suspicious port — is not a verdict. Real users on VPNs, corporate networks, or unusual devices can trigger one odd signal. BotRefund treats each signal as independent evidence, then cross-checks whether other browser, network, device, and behavior signals tell the same story. Only when the complete pattern aligns does the AI model classify the visit as bot or human.
This matters because Google's own invalid-traffic filters catch only a subset. Google filters what it detects, but advertisers still need account-level monitoring to protect lead quality and bidding data, as third-party analyses note. The gap is what dedicated detection layers aim to close.
Main detection signal categories
Click and engagement behavior
- Ghost click detection — catches click activity that happens without the natural sequence of human intent.
- Honeypot trap interactions — watches for bots that respond to hidden or intentionally deceptive page elements.
- Absence of clicks or scrolling — highlights sessions that stay too static to match a real browsing journey.
- Unnatural session durations — catches visit lengths that are too short, too long, or too uniform to be human.
Pointer and motion dynamics
- Robotic linear mouse movements — flags unnaturally straight pointer paths that rarely appear in real user sessions.
- Absence of humanlike mouse tremor — looks for the tiny imperfections and jitter typical of human movement.
- Superhuman input speed (<1ms) — identifies interactions that happen faster than a person could realistically perform.
- Grid-aligned movement patterns — detects movement that snaps to precise lines or blocks instead of natural curves.
Network, VPN, and geolocation vectors
One example is the Suspicious Ports check. It looks for mismatches between a visitor's connection, location, language, and timing that a real browsing session does not normally create. Proxy rotation, location masking, or browser spoofing can make separate network facts disagree. This signal is kept as evidence — not a verdict — and cross-checked against the other 105 checks.
Biometric and behavioral interactions
The Monitor Sync Anomaly check examines whether clicks, scrolls, and timing carry the varied hesitation and micro-pauses shaped by reading and decision-making. Scripts can send events but struggle to reproduce the natural variability of real people. Again, this is one piece of evidence fed into the AI model.
Why single signals fail and corroboration matters
Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. A rule-based system that blocks on one signal generates false positives. BotRefund's architecture keeps each signal as independent evidence, tests whether other signals support the same story, and lets the AI prediction weigh the complete pattern. The company states this corroboration — not any single browser tell — is why it reaches 99% accuracy.
What Google's own filters catch vs. miss
Google's invalid traffic guidance covers tools, bots, spiders, crawlers, deceptive software, accidental clicks, and other activity that is not genuine user interest. However, Google filters only what it detects. Advertisers still need account-level monitoring to protect lead quality and bidding data. Specialized third-party systems add detection layers for ghost clicks, honeypot interactions, robotic pointer paths, superhuman speed, grid-aligned movement, static sessions, uniform durations, network mismatches, and biometric timing anomalies — signals that may fall outside Google's default filters.
Step-by-step: how to audit and improve detection accuracy
- Install a detection script that captures behavioral, network, and biometric signals. BotRefund adds to a site in about one minute with no credit card required.
- Run a free AI audit. The system collects 106 independent checks across a sample of traffic.
- Review the evidence report. Each flagged session shows which signals fired and how they corroborate.
- Export the report and send it to your Google or Meta representative. Use the video proof and signal breakdown to open a billing dispute.
- Track refund approval rates. BotRefund reports an 83% customer success rate for refund claims submitted to ad platforms.
- Enable ongoing protection. The script continues monitoring live traffic and building evidence for future claims.
Common mistakes that reduce detection accuracy
- Relying only on Google's automatic filters and skipping account-level monitoring.
- Using a single-signal rule (e.g., block all VPN IPs) which creates false positives.
- Not preserving video proof and signal logs needed for refund disputes.
- Waiting too long — refunds can be claimed on Google Ads spend dating back to 2017, but platforms have dispute windows.
- Ignoring biometric and network signals that catch sophisticated bots mimicking basic click patterns.
Limitations and when detection accuracy claims don't apply
- The 99% accuracy figure is a client claim from BotRefund's own model evaluation; independent verification is not provided in the source pack.
- The 83% refund success rate reflects customers who pursued claims; it does not guarantee every claim succeeds.
- Detection works on traffic that reaches the website; it cannot catch bots that never load the page (e.g., pre-click impression fraud).
- Corporate networks, privacy tools, and unusual devices can still produce edge cases that require human review.
- Refund recovery depends on Google and Meta dispute processes, which the advertiser does not control.
Key facts
| Metric | Detail | Source |
|---|---|---|
| Independent detection checks | 106 | S3, S5 |
| Claimed AI prediction accuracy | 99% | S3, S5 |
| Customer refund success rate | 83% | S1 |
| Refund lookback window | Google Ads spend dating back to 2017 | S1 |
| Setup time | About 1 minute to add to website | S1, S2 |
| Free audit availability | Yes, no credit card required | S1, S2 |
| Platforms covered | Google and Meta | S1 |
| Estimated budget lost to bot clicks | Up to 20% of Google and Meta ad budget | S1 |
FAQ
How many signals does BotRefund check per visit?
106 independent checks across browser, network, device, and behavior evidence.
Does a single suspicious signal mean the visitor is a bot?
No. Each signal is kept as evidence, not a verdict. The AI model weighs the complete pattern across all signals.
Can I get refunds for past ad spend?
Yes. BotRefund recovers bot-click refunds from Google Ads spend dating back to 2017.
What proof do I need to submit a refund claim?
Video proof for each bot click and a signal breakdown report exported from the audit.
How long does setup take?
About one minute to add the script to your website; no credit card required for the free audit.
What if my traffic uses VPNs or corporate networks?
Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund cross-checks network signals against browser, device, and behavior data to avoid false positives.
Does this replace Google's invalid traffic filters?
No. It adds account-level monitoring for signals Google's default filters may miss, such as ghost clicks, honeypot interactions, robotic pointer paths, superhuman speed, grid-aligned movement, static sessions, uniform durations, network mismatches, and biometric timing anomalies.
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