What Proof Does Automated Software Provide for Refund Claims?

Automated refund software does not just flag suspicious traffic — it builds a structured evidence packet that ad platforms can audit. BotRefund, for example, captures video proof of each bot click, logs the click IDs (GCLID for Google, FBCLID for Meta) that tie a visit to a billed impression, and records 106 independent browser, network, device, and behavioral signals. The software then cross-checks those signals, weights them through an AI model, and exports a report formatted to each platform's dispute specification.

The result is a dossier that shows how a visit failed to behave like a human: missing mouse tremor, superhuman click speed, grid-aligned pointer paths, ghost clicks without intent, honeypot interactions, and session durations that are too short, too long, or too uniform. Each anomaly is recorded as an independent fact, not a verdict, and the final report presents the corroborated pattern that Google's Click Quality team or Meta's billing support can review against their own invalid-traffic definitions.

What Automated Refund Evidence Actually Contains

An evidence package has three layers: raw signals, correlated findings, and platform-ready formatting. Raw signals come from client-side JavaScript that runs in the visitor's browser — no server-side inference. Correlated findings come from the detection engine checking whether multiple independent signals tell the same story. Platform-ready formatting means the export includes the exact fields Google and Meta ask for: click IDs, timestamps, IP context, device fingerprints, and a narrative summary of the behavioral anomalies.

How BotRefund Builds Its Evidence Package

The process starts the moment a visitor lands on a page with the tracking script installed. The script observes 106 independent checks grouped into seven behavioral families: click behavior, trap behavior, pointer behavior, motion behavior, speed behavior, path behavior, engagement behavior, and session behavior. Each check produces a binary or scored signal — for example, "ghost click detected" or "mouse tremor absent." No single signal triggers a refund claim. Instead, the AI prediction layer weighs the complete pattern across browser, network, device, and behavior evidence to reach a 99% accuracy rating for bot vs. human classification.

The 106-Point Detection Framework

BotRefund organizes its checks into eight categories that map to observable browser behaviors:

• Click behavior — Ghost click detection catches clicks that fire without the natural sequence of human intent (move, hover, press, release).
• Trap behavior — Honeypot trap interactions watch for bots that respond to hidden or deceptive page elements real users never see.
• Pointer behavior — Robotic linear mouse movements flag unnaturally straight paths; real hands produce micro-curves.
• Motion behavior — Absence of humanlike mouse tremor looks for the tiny jitter that living muscle produces.
• Speed behavior — Superhuman input speed (<1 ms) identifies interactions faster than a person can physically perform.
• Path behavior — Grid-aligned movement patterns detect snapping to precise lines or blocks instead of natural curves.
• Engagement behavior — Absence of clicks or scrolling highlights sessions that stay too static to match a real browsing journey.
• Session behavior — Unnatural session durations catch visits that are too short, too long, or too uniform to be human.

Each category contains multiple independent checks (for example, scrollbar-width leak and clean-context iframe are two of the 106). The system treats every check as a single objective fact, then cross-checks it against the others before the AI model weighs the full pattern.

Behavioral Signals That Platforms Accept

Google and Meta do not publish a checklist, but their invalid-click definitions map closely to the signals above. Google's categories — competitor click activity, publisher click fraud, bot traffic and web scrapers — all leave behavioral fingerprints. A competitor's manual clicks still show human tremor but may reveal abnormal session duration or referral patterns. Publisher fraud via background scripts typically lacks scroll, mouse movement, and click-sequence integrity. Scrapers using headless Chrome or residential proxies often fail the motion, speed, and path checks even when their IPs look residential. The evidence package makes those fingerprints explicit and auditable.

Technical Proof Components: GCLID, FBCLID, Video, and Logs

Four concrete artifacts anchor every dispute:

• GCLID / FBCLID logs — The click identifiers that Google Ads and Meta attach to each paid visit. BotRefund captures them automatically so the refund request can reference the exact billed clicks.
• Client-side behavioral proof logs — Timestamped event streams showing every mouse move, click, scroll, and focus change, plus the 106 signal evaluations for that session.
• Video proof — A session replay that visualizes the bot's behavior (or lack thereof) for human reviewers at the platform.
• Audit-ready dispute report — A formatted PDF/CSV that summarizes the correlated anomalies, lists the click IDs, and maps findings to the platform's invalid-traffic categories.

All four are generated from the same client-side collection, so there is no gap between what the script saw and what the report claims.

How Evidence Gets Formatted for Google vs. Meta

Google's Click Quality team expects a manual investigation form backed by GCLID lists, IP logs, and a narrative explaining why the clicks fall outside normal user behavior. Meta's billing support uses a similar form but references FBCLID and places more weight on conversion-pixel integrity — hence BotRefund's emphasis on "pixel poisoning" protection. The software exports two report templates: one structured for Google's dispute fields (click IDs, date ranges, campaign IDs, anomaly summary) and one for Meta's (FBCLID, pixel event logs, lead-form timestamps). The underlying evidence is identical; only the packaging changes.

Limitations and What Evidence Cannot Prove

Automated evidence proves that a visit behaved like a bot; it cannot prove who sent the bot or why. It also cannot recover spend that platforms classify as "accidental clicks" (double-clicks, fat-finger taps) because those still show human behavioral signatures. Privacy tools, corporate proxies, and unusual devices can produce false-positive signals, which is why BotRefund keeps each signal as evidence rather than a verdict and requires cross-check corroboration. Finally, the evidence only covers traffic that reaches the landing page with the script installed — it cannot see clicks that bounce before the script loads or traffic on platforms where the script is not deployed.

Key Facts

Fact	Detail	Source
Detection signals	106 independent checks across browser, network, device, behavior	S3, S4
Behavioral categories	Click, trap, pointer, motion, speed, path, engagement, session	S2, S8
Claimed classification accuracy	99% bot vs. human	S3, S4
Core proof artifacts	GCLID/FBCLID logs, behavioral event streams, video replay, audit-ready report	S2, S5, S6, S7
Platform targets	Google Ads Click Quality team, Meta billing support	S2, S6
Setup time	About one minute to add script	S2
Historical reach	Google Ads refunds back to 2017	S2

FAQ

Does the evidence work for both search and social campaigns?

Yes. GCLID covers Google Search, Display, and YouTube; FBCLID covers Facebook, Instagram, and Audience Network. The behavioral signals are platform-agnostic because they measure browser behavior, not traffic source.

Can I use this evidence if I already filed a dispute and got denied?

You can reopen a dispute with new evidence. The video replay and correlated 106-signal analysis often supply the granularity that a first submission lacked.

What if my site uses a single-page app or heavy AJAX?

The client-side script tracks DOM events and navigation changes regardless of page-load model, so behavioral signals still fire. Click IDs are captured on the initial ad landing.

How far back can I claim refunds?

BotRefund states Google Ads refunds can reach back to 2017. Meta's window is typically shorter; check current policy at time of filing.

Does the script slow down my page?

The vendor claims lightweight deployment (about one minute to add) but does not publish specific performance metrics. Test in staging before full rollout.

What happens if a real user triggers a signal (e.g., accessibility tool)?

Each signal is kept as evidence, not a verdict. The AI model weighs the full pattern; isolated anomalies from privacy tools or assistive tech rarely produce a bot classification on their own.

Can I export raw logs for my own analysis?

Yes. The platform provides client-side behavioral proof logs and click-ID exports that you can feed into BI tools or share with an agency.

Further reading and comparison sources

These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.

• Best AI for Refund Automation (2026): 6 Tools Compared
• Warranty Claim Process | Tips to Optimize Fast
• Best Ecommerce Refund Automation Tools (2026 Guide)

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