Seatext library / BotRefund evidence
How Accurate Is BotRefund in Detecting Automation? A Practical Breakdown
BotRefund claims 99% accuracy in detecting automated traffic by running 106 independent browser, network, device, and behavioral checks that feed into an AI prediction model. No single signal triggers a verdict; each anomaly is...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund says it identifies automated visits with 99% accuracy. That figure comes from a system that runs 106 independent checks across browser internals, network attributes, device fingerprints, and behavioral biometrics, then weighs the complete pattern through an AI model instead of relying on any single tell. A lone anomaly — such as a missing browser API or an unusually fast click — is kept as evidence, not a verdict, and is cross-referenced against the other signals before a final classification is made.
How BotRefund's Detection System Works
The detection pipeline has three layers. First, the client-side collector runs 106 checks during each visit. These checks probe browser APIs, timing behaviors, pointer dynamics, and navigation patterns. Second, each check emits an independent evidence signal — for example, whether the window.open method behaves like a real browser or shows signs of tampering. Third, an AI prediction model ingests all signals simultaneously and evaluates how they fit together across four dimensions: browser, network, device, and behavior. The model outputs a bot-or-human classification with a confidence score.
This design avoids the classic pitfall of rule-based detectors: a single oddity (a privacy extension, a corporate proxy, an unusual device) does not automatically flag a visitor. Instead, the model asks whether the entire constellation of signals tells a consistent automation story.
The 106 Independent Checks: What They Cover
BotRefund groups its checks into eight behavioral categories. Each category contains multiple specific tests that run in parallel:
- Click behavior — Ghost click detection catches clicks that lack the natural human intent sequence.
- Trap behavior — Honeypot trap interactions watch for bots responding to hidden or deceptive page elements.
- Pointer behavior — Robotic linear mouse movements flag unnaturally straight pointer paths.
- Motion behavior — Absence of humanlike mouse tremor looks for the tiny imperfections and jitter typical of real movement.
- Speed behavior — Superhuman input speed (<1ms) identifies interactions faster than a person could perform.
- Path behavior — Grid-aligned movement patterns detect movement snapping to precise lines or blocks instead of natural curves.
- Engagement behavior — Absence of clicks or scrolling highlights sessions too static to match a real browsing journey.
- Session behavior — Unnatural session durations catch visit lengths that are too short, too long, or too uniform.
These categories are sourced directly from BotRefund's public detection documentation and represent the observable behavioral surface the system monitors.
Why Corroboration Beats Single Signals
BotRefund's documentation emphasizes that "a single anomaly is not a bot verdict." Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. The system keeps each signal as evidence and cross-checks it against independent browser, network, device, and behavior data. Only when multiple independent signals support the same story does the AI model assign a high-confidence bot classification.
This approach mirrors how fraud analysts manually investigate: they look for converging indicators rather than smoking guns. The difference is that BotRefund automates the convergence check across 106 signals in real time.
Specific Detection Signals Explained
Playwright Init Scripts
Automation frameworks like Playwright often patch or hide browser APIs to avoid detection. The Playwright Init Scripts check looks for mismatches that a real browsing session does not normally create — for example, when a patched API behaves inconsistently when probed from another angle. A normal browser runs standard APIs as designed; its built-in properties, permissions, and rendering contexts remain consistent without needing to hide automation.
window.open Tamper
Scripts can send clicks and scrolls, but they struggle to reproduce the varied timing, movement, and hesitation of real people. The window.open Tamper check looks for a mismatch in how the window.open method behaves under automation versus a genuine session.
Impossible Tab Speed
This check flags tab-switching or navigation events that occur faster than humanly possible. Real visitors produce imperfect, varied behavior: pauses, hesitation, natural movement, and interactions shaped by reading and decision-making. Automation scripts often execute sequences at machine speed, leaving a timing fingerprint.
Each of these signals is one of the 106 independent checks. None alone determines the outcome; each feeds the AI model's pattern evaluation.
Accuracy in Practice: What the Numbers Mean
The 99% accuracy claim appears repeatedly in BotRefund's detection documentation: "By seeing how all signals fit together, it identifies a visit as bot or human with 99% accuracy." This figure reflects the AI model's classification performance on the combined signal set, not any individual check.
A published case study provides concrete context: a neobank client (FinTrust) recovered $140,000 in ad spend, with an average bot click rate of 14% and an 18% conversion rate increase after suppressing automated conversion events. The case study notes that "BotRefund audit trails are the gold standard that Meta ad reps accept," suggesting the evidence quality meets platform review thresholds.
BotRefund also states it can recover bot-click refunds from Google Ads spend dating back to 2017, and that setup takes about one minute with no credit card required for the free audit.
Limitations and False Positives
BotRefund explicitly acknowledges scenarios that can produce unexpected signals for genuine users:
- Privacy tools (anti-fingerprinting extensions, hardened browsers)
- Corporate networks (proxies, VPNs, zero-trust architectures)
- Travel (roaming, carrier-grade NAT, varying IP reputation)
- Unusual devices (rare browser versions, assistive technologies, embedded browsers)
Because the system treats each anomaly as evidence rather than a verdict, these edge cases are less likely to trigger false positives than single-rule detectors. However, no system eliminates false positives entirely. Advertisers should review flagged sessions in the audit dashboard before submitting refund claims, especially for high-value campaigns.
How to Verify Detection on Your Own Traffic
- Request a free bot audit from BotRefund's website. The audit runs live on your site during a scheduled call.
- Add the BotRefund script to your website (reported as a one-minute process, no credit card required).
- Let the system collect traffic for a representative period — typically a few days to a week depending on volume.
- Review the audit dashboard: each flagged session shows the specific signals that contributed to the classification, along with a video replay of the visit.
- Compare flagged sessions against your CRM outcomes (lead quality, contactability, sales progression) to validate that the detections align with business reality.
- If satisfied, submit refund claims to Google and Meta using BotRefund's organized evidence dossiers.
The free audit is the lowest-risk way to test accuracy on your actual traffic before committing to a paid plan.
Key Facts
| Metric | Detail | Source |
|---|---|---|
| Claimed classification accuracy | 99% (AI model across 106 signals) | S1, S6, S7 |
| Number of independent checks | 106 | S1, S6, S7 |
| Signal categories | Click, Trap, Pointer, Motion, Speed, Path, Engagement, Session | S2, S5, S9 |
| Detection dimensions | Browser, Network, Device, Behavior | S1, S6, S7 |
| Single-anomaly policy | Evidence only, not a verdict; cross-checked | S1, S6, S7 |
| Setup time for free audit | ~1 minute, no credit card | S2, S5, S9 |
| Refund lookback window | Google Ads spend back to 2017 | S2, S5, S9 |
| Case study recovery (FinTrust) | $140,000 refunded, 14% bot click rate, +18% conversion | S4 |
| Platform acceptance | Audit trails accepted by Meta ad reps | S4 |
Frequently Asked Questions
Does BotRefund block bots or just detect them?
BotRefund's core product is detection and evidence collection for refund claims. It also offers Pixel Protection to keep fraudulent sessions from distorting conversion data, and suppression signals to stop platforms from optimizing toward bot traffic. It does not function as a WAF or traffic blocker at the network edge.
Can privacy-focused browsers trigger false positives?
Yes, hardened browsers and anti-fingerprinting tools can produce anomalous signals. BotRefund's cross-check design mitigates this: a privacy tool might trip one browser check, but the network, device, and behavior signals will usually remain human-consistent, so the AI model does not classify the visit as a bot.
How does the 99% accuracy claim hold up across different traffic sources?
The claim is based on the AI model's evaluation of the full 106-signal pattern. Accuracy can vary by traffic mix (search vs. social, mobile vs. desktop, geographic region). The free audit lets you measure performance on your specific traffic before relying on the system for refund claims.
What evidence does BotRefund provide for refund submissions?
Each flagged session includes the specific signals that fired, a video replay of the visit, and an organized evidence dossier formatted for Google and Meta billing dispute processes. The case study notes Meta ad reps accept these audit trails as evidence.
Is there a minimum ad spend to use BotRefund?
The pricing tiers shown on the site start at "Under $10,000/mo" and scale up to "Over $5M/mo." Enterprise sales are handled separately. The free audit is available regardless of spend tier.
How often are the 106 checks updated?
BotRefund does not publish a fixed update cadence. Because the checks target automation framework behaviors (Playwright, Puppeteer, Selenium, custom headless setups), updates likely track new framework releases and evasion techniques. The AI model also retrains on new signal patterns.
Can I run BotRefund alongside other bot detection tools?
Yes. The script is lightweight and designed to coexist with other analytics and security tags. Running multiple detectors can provide a useful cross-reference, though you should deduplicate refund claims to avoid double-counting the same invalid clicks.
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