Advertising fraud is not new. However, it has become more sophisticated, more profitable for those who practice it, and more damaging for those who suffer from it. If you invest in Google Ads or Meta Ads, there is a real chance that part of that budget never reached a human user. In fact, according to the Ad Fraud Report 2025 from Opticks Security, invalid traffic ranges from 2.18% in search (SEM) to 25.37% in the most exposed sectors. This article explains exactly how this fraud works, who is behind it, and what can be done about it.
What are fraudulent clicks?
Fraudulent clicks are advertising interactions generated without a real intention to purchase or convert. There is no user behind them. Or if there is, they act with a vested interest to harm the advertiser.
In the industry, this is referred to as click fraud, invalid traffic (IVT, Invalid Traffic), or false clicks. All describe the same thing: visits that consume budget without providing any value.
The difference between a legitimate click and a fraudulent one is simple in theory: the former comes from someone interested in what you are advertising. The latter comes from a bot, a vengeful competitor, or someone who gets paid to generate clicks without caring about your product.
Quick definition: Click fraud is the artificial or malicious generation of clicks on digital ads with the aim of exhausting the advertiser's budget, inflating metrics, or generating fraudulent income.
Why do fraudulent clicks exist?
Because there is money at stake. It’s that simple.
Competitors exhausting budgets
A competitor can repeatedly click on your ads until your daily budget is exhausted. The result: your ads disappear from the search engine and theirs rise in position. It is unfair competition, illegal in many countries, and yet it happens constantly.
Bot networks
Bots are automated programs designed to simulate the behaviour of a real user. They click, browse the web, stay on the page for a few seconds, and leave. All automated, all fake.
Click farms
Click farms are organised structures, usually in countries with cheap labour, where dozens or hundreds of people manually click on ads in exchange for minimal payment. The fraud here is human, not automated, making it harder to detect.
Affiliate fraud
In affiliate networks, the publisher gets paid for each click or conversion generated. Some unscrupulous publishers artificially inflate these figures to earn more commissions. The advertiser pays for results that never existed.
Incentivised fraud
Real users who click on ads in exchange for rewards (points, discounts, access to content). They generate real traffic but with zero intention to purchase.
How fraudulent clicks work in Google Ads
In Google Ads, fraud is primarily executed through automated searches. A bot launches queries on Google with the exact keywords that trigger your ads, clicks, and repeats the process.
To avoid detection, the bot uses:
- IP rotation using proxies or VPNs, so each click appears to come from a different user.
- Virtual devices or emulators that simulate different operating systems and browsers.
- Human behaviour emulation, including mouse movements, variable dwell times, and scrolling patterns.
The result is a campaign with an apparently high CTR, skyrocketing costs, and a number of conversions that do not correspond to anything.
How fraudulent clicks work in Meta Ads
Meta Ads presents a somewhat different scenario. Here, fraud does not always come from searches: it comes from low-quality traffic, fake accounts, and engagement farms.
The main vectors in Meta are:
- Bots in interaction campaigns: automated accounts that like, click, or interact with ads to inflate metrics.
- Incentivised clicks: real users who receive compensation for interacting with ads.
- Low-quality app traffic: in app install or conversion campaigns, certain publishers within the Meta network generate completely fictitious installs or clicks.
Meta has its own detection systems, but the volume of traffic it manages makes it impossible to capture all the fraud. According to the Ad Fraud Report 2025 from Opticks, invalid traffic reaches 23% on TikTok and 40% on Android apps, compared to 2% on Meta.
Most common types of advertising fraud
Click Fraud
The classic. Manual or automated clicks on paid ads aimed at exhausting the advertiser's budget or generating fraudulent income for the publisher.
Bot Traffic
Visits generated by bots that simulate human sessions. They can pass as legitimate users in basic measurement systems.
Ad Stacking
Multiple ads are stacked on top of each other in the same space. Only the top ad is visible, but all register impressions and, in some cases, clicks.
Domain Spoofing
A fraudulent publisher passes off their low-quality inventory as if it were from a premium media outlet. The advertiser pays a premium price for low-quality traffic.
Click Injection
Fraud specific to the mobile ecosystem. A malicious app detects when the user is installing another app and launches a fake click just before the installation is completed, claiming the conversion.
Click Spamming
Also known as click flooding. It involves sending a massive volume of fake clicks from real user devices without their knowledge, usually through apps that run processes in the background.
Invalid Traffic (IVT)
Umbrella term that encompasses all traffic that does not come from human users with genuine intent. IVT is divided into General Invalid Traffic (GIVT), which is easier to detect, and Sophisticated Invalid Traffic (SIVT), designed to evade standard detection systems.
How to detect fraudulent clicks
This is the practical part. Here are the indicators that should raise all alarms:
- Abnormally high CTR for the sector or type of campaign.
- Non-existent conversions despite a high volume of clicks.
- Skyrocketing bounce rate: bots arrive and leave within seconds.
- Average session duration close to zero.
- Suspicious geographical concentration: an unusual volume of clicks from locations that make no sense for your business.
- Repetitive patterns: the same IP ranges, the same times, the same devices.
- Unusual ratio between impressions and clicks during certain time slots.
Early warning checklist:
- Does your CTR exceed the historical average by 50% or more?
- Do campaigns exhaust the budget much earlier than expected?
- Has the conversion percentage dropped without changes on the landing page?
- Do you see traffic spikes at night without justification?
- Does Google Analytics show 0-second sessions in relevant volume?
If you answered yes to two or more questions, you have a problem.
Signs that your company may be losing money due to advertising fraud
- The cost per acquisition (CPA) rises without you having changed anything.
- Campaigns perform worse for no apparent reason.
- Google Ads reports clicks that Google Analytics does not register.
- There are geographical locations in the reports that do not match your target audience.
- Engagement metrics (time on page, pages per session) are abnormally low.
- A/B tests yield inconsistent results with expected behaviour.
- In fact, lead generation forms are a magnet for bots: they account for 46.7% of their invalid conversions, compared to 13% in e-commerce (Opticks Ad Fraud Report 2025).
How much money can a company lose due to fraudulent clicks
Figures vary by source, but the order of magnitude is consistent: advertising fraud results in a global loss of tens of billions of euros per year. For a specific company, the impact can represent between 10% and 40% of the budget invested in paid campaigns.
But the direct loss is only part of the problem:
- Loss of real opportunities: every euro spent on a fraudulent click is a euro that did not reach a user with purchase intent.
- Data bias: if your analysis includes invalid traffic, the decisions you make about bids, audiences, and creatives will be based on contaminated information.
- Incorrect business decisions: campaigns that seem not to work may be working perfectly, only drowned by fraud.
Illustrative example: a company with €10,000/month in Google Ads and 20% fraudulent traffic would be losing €2,000/month – €24,000 a year. The actual percentage varies greatly.
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