Ad Fraud Detection for Online Ads: How It Works and What You Can Recover

Ad fraud detection for online ads is the process of identifying automated traffic — bots — that click on your paid campaigns without any human intent. These fake clicks drain budget, distort performance data, and inflate costs per acquisition. The detection works by analyzing behavioral signals (mouse movement, click timing, scroll depth) and technical signals (network consistency, browser fingerprint, port anomalies) to separate real visitors from scripts. When bot clicks are proven, advertisers can file billing disputes with Google Ads and Meta to recover wasted spend.

What ad fraud detection actually means

Ad fraud detection is not a single filter. It is a layered evidence-gathering system. Each visit to your landing page leaves a trail: how the mouse moved, how fast clicks happened, whether the session duration looks human, whether the network location matches the browser language, and dozens of other micro-signals. A detection engine collects these signals, weighs them together, and assigns a probability that the visitor was automated. The goal is not to block traffic in real time — ad platforms control the impression — but to build a documented case that specific clicks were invalid so you can request a refund.

Why it matters for Google and Meta advertisers

Bot clicks steal up to 20% of your Google and Meta ad budget, according to BotRefund's data. That waste compounds: you pay for the click, you pay for the downstream optimization that learns from bad data, and you lose the opportunity to show the ad to a real prospect. Most advertisers rely on the platforms' built-in invalid traffic filters, but those filters are conservative — they only remove the most obvious fraud. The remainder still bills to your account. Independent detection fills that gap by catching sophisticated bots that mimic human behavior well enough to pass the platform's first pass.

How bot detection works: the 106 independent checks

BotRefund runs 106 independent checks across four categories: browser behavior, network and geolocation, device fingerprint, and session patterns. No single check decides the verdict. Instead, each check contributes one piece of evidence. The engine cross-references them — for example, a visit that shows superhuman click speed (<1ms) but also has a consistent residential IP and normal mouse tremor might still be human. A visit that shows superhuman speed, grid-aligned mouse paths, no scroll activity, and a data-center IP mismatch gets flagged with high confidence. The final prediction model weighs the complete pattern and claims 99% accuracy.

Behavioral signals

• Ghost click detection: Clicks that fire without the natural sequence of human intent — no hover, no approach movement, no pre-click pause.
• Honeypot trap interactions: Bots often click hidden or deceptive page elements that real users never see.
• Robotic linear mouse movements: Unnaturally straight pointer paths that rarely appear in real sessions.
• Absence of humanlike mouse tremor: Real hands produce micro-jitter; automated scripts often move in perfect curves or straight lines.
• Superhuman input speed (<1ms): Interactions faster than a person could physically perform.
• Grid-aligned movement patterns: Movement that snaps to precise lines or blocks instead of natural curves.
• Absence of clicks or scrolling: Sessions that stay too static to match a real browsing journey.
• Unnatural session durations: Visits that are too short, too long, or too uniform to be human.

Network and technical signals

One example is the Suspicious Ports check. A real visitor's connection, location, language, and timing normally agree. Proxy rotation, location masking, or browser spoofing can make separate network facts disagree. This check looks for that mismatch. It is kept as evidence — not a verdict — and cross-checked against the other 105 signals. Privacy tools, travel, corporate networks, and unusual devices can produce anomalies for genuine people, so the system requires corroboration before labeling a visit as bot.

Main types of ad fraud caught

The detection covers the fraud types that most directly waste click budget on Google and Meta:

• Click fraud: Automated scripts or click farms repeatedly clicking ads to drain competitor budgets or generate revenue for fraudulent publishers.
• Impression fraud (when paired with click data): Bots that load ads to create fake inventory, then click to simulate engagement.
• Affiliate fraud: Bots that simulate conversions or lead forms to trigger affiliate payouts.
• Retargeting pollution: Bots that visit your site after clicking an ad, poisoning your retargeting audiences with non-human profiles.

The system does not directly detect viewability fraud (ads served in non-viewable placements) or domain spoofing unless those visits also generate clicks that reach your landing page.

The refund recovery process

Detection is only half the value. The second half is turning evidence into money back. The workflow:

1. Install the script: Add BotRefund to your website in about one minute. No credit card required.
2. Run the free AI audit: The system collects visit data and builds a report showing which clicks were bots, with video proof for each flagged session.
3. Export the report: Download the evidence package formatted for Google Ads and Meta billing dispute submissions.
4. Submit to platform reps: Send the report to your Google or Meta representative (or through the platform's invalid click refund form).
5. Negotiation and approval: BotRefund's team assists with the dispute. They claim an 83% success rate across client refund claims submitted to ad platforms, with refunds recovered from Google Ads spend dating back to 2017.

The process works for accounts of any size. Pricing tiers are based on monthly Google/Meta spend: under $10K/mo, $10K–$50K/mo, $50K–$250K/mo, $250K–$1M/mo, $1M–$5M/mo, and over $5M/mo. Enterprise plans add a dedicated recovery, protection, and escalation plan.

Key facts

Metric	Detail	Source
Bot click waste estimate	Up to 20% of Google and Meta ad budget	S1
Independent detection checks	106 signals across browser, network, device, behavior	S6
Claimed prediction accuracy	99%	S6
Refund approval rate	83% of customers successfully get a refund	S1
Refund lookback window	Google Ads spend dating back to 2017	S1
Setup time	About 1 minute to add to website	S1
Free audit	No credit card required	S1
Pricing model	Tiered by monthly Google/Meta ad spend	S1

Limitations and when this doesn't apply

• Platform coverage: Refund recovery is limited to Google Ads and Meta (Facebook/Instagram). Other ad platforms (TikTok, LinkedIn, Twitter/X, programmatic DSPs) are not supported for automated dispute filing.
• Click-only scope: The system detects bots that reach your landing page. It cannot detect fraud that occurs entirely within the ad platform's ecosystem (e.g., impression fraud on third-party publisher sites where the bot never clicks through).
• No real-time blocking: The script does not prevent bots from clicking your ads. It documents the clicks after they happen so you can reclaim the spend.
• Approval not guaranteed: The 83% success rate is an aggregate across clients. Individual disputes may be denied if the platform's review team disagrees with the evidence.
• Privacy and compliance: The script collects behavioral and network data. You must disclose this in your privacy policy and comply with GDPR, CCPA, and other applicable regulations.
• Enterprise features: Dedicated escalation plans and custom recovery mapping are only available on Enterprise tiers.

Common terminology

• Invalid traffic (IVT): The industry term for clicks or impressions generated by bots, crawlers, or other non-human sources.
• General invalid traffic (GIVT): Easily identifiable bots like search engine crawlers, monitoring tools, and known data-center IP ranges.
• Sophisticated invalid traffic (SIVT): Bots that mimic human behavior — residential proxies, mouse movement simulation, device fingerprint spoofing — requiring advanced detection.
• Click fraud: A subset of IVT where the intent is to waste an advertiser's budget or generate revenue for a fraudulent publisher.
• Honeypot: A hidden page element (link, button, form field) that real users cannot see but bots interact with, revealing their automated nature.
• Billing dispute / invalid click refund: The formal process of requesting a credit from Google Ads or Meta for clicks determined to be invalid.

FAQ

How is this different from Google's and Meta's built-in invalid click filters?

Platform filters catch GIVT — known bots, data-center IPs, obvious patterns. They are conservative to avoid false positives. Independent detection adds a second layer that catches SIVT: bots using residential proxies, realistic mouse simulation, and behavioral mimicry that pass the platform's first pass but leave micro-anomalies across 106 signals.

Do I need technical skills to install the detection script?

No. The script is a single JavaScript snippet added to your site header or via Google Tag Manager. Setup takes about one minute. No credit card is required to start the free audit.

What evidence do I actually send to Google or Meta?

The system exports a report with flagged sessions, timestamps, IP data, behavioral anomaly details, and video replay of each bot session. This package is formatted for the platforms' invalid click refund submission process.

How far back can I recover refunds?

BotRefund states they recover Google Ads spend dating back to 2017. The practical lookback depends on each platform's dispute policy and your account history.

What happens if a dispute is denied?

You can re-submit with additional evidence. BotRefund's team assists with escalation on Enterprise plans. The 83% approval rate is an aggregate; individual outcomes vary.

Does this work for programmatic or display campaigns on non-Google/Meta platforms?

The detection script will still flag bot visits from any traffic source, but the automated refund recovery workflow only supports Google Ads and Meta. For other platforms, you would need to manually submit the evidence to their support teams.

Is the 99% accuracy claim verified independently?

The 99% figure comes from BotRefund's own model evaluation. No third-party audit is referenced in the source material. Treat it as a vendor claim, not an independently verified benchmark.

Further reading and comparison sources

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

• Top Ad Fraud Detection Companies (2026) - Business of Apps
• Ad Fraud 2026: Detection & Prevention Guide
• What is Ad Fraud Detection? - Anura.io

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