#invalid clicks

You know you need an answer when your campaigns don’t perform as you expect them to. Or maybe they do perform, but just on dashboards? 

Click fraud is no longer limited to just basic bot traffic. In 2026, it has evolved into more sophisticated threats that many traditional detection tools are not equipped to identify. 

Fraudsters now deploy AI-powered bots that simulate real user behaviour to evade behavioural detection. They operate across performance marketing ecosystems, including Google Search, display, GDN, Pmax, Meta, affiliate networks, app install campaigns, lead generation, and re-engagement campaigns. 

Digital ad fraud is growing at breakneck speed. This year, marketers are expected to lose over $114 billion to ad fraud collectively. The situation is scary, and the future seems bleak. However, many marketers and brands still successfully turn a blind eye to ad fraud, and they do it because of a lack of awareness. Awareness of the different types of ad fraud can help you be more vigilant and cautious towards the rising threats and help you save your ad budgets from getting wasted. With this blog, we hope to help you bring this awareness.

Are you proactively analyzing the ad traffic of your campaigns?  

Is it really coming from genuine users or just being generated by bots? 

Yes, a significant portion of traffic that makes your ad campaigns seem successful could be invalid traffic. According to mFilterIt’s analysis featured in FICCI Report 2025, invalid traffic contributes to as much as 30–50% of activity across digital channels, directly distorting performance metrics and draining ad budgets. 

For years, marketers have relied on click fraud validation as proof that their campaigns are reaching real users. If the clicks look clean, the traffic must be genuine, right? It’s been the comfort metric, the checkpoint that promises accountability in a noisy digital ecosystem. 

But here’s the catch: validating just click fraud doesn’t guarantee real outcomes. Behind a perfectly validated click could be a fake lead, a disengaged visitor, or a bot sophisticated enough to mimic human behavior. You may have solved surface-level traffic hygiene, but not for what truly drives performance authentic engagement and meaningful conversions. 

While online fraud perhaps started with email spamming, it has come a long way. Today, ad fraud takes many forms, and it costs digital advertisers several billion dollars. According to Statista Reports, within the 5 years between 2023 & 2028, the costs due to digital advertising fraud will increase from $88 billion to $172 billion. The lure of making quick money has motivated modern fraudsters to employ sophisticated techniques to commit fraud.

Every click matters; every click costs money. But what if many of those clicks aren’t even from real users? How do you identify the clicks that you get on each campaign as genuine and from the right audience? 

Click fraud has emerged as one of the most complex, persistent threats in the world of digital advertising. It has now evolved into a sophisticated, multi-channel challenge that silently erodes campaign performance across both web and app ecosystems. From automated bots and click farms to SDK spoofing and device manipulation, fraudsters try to exploit every possible entry point of the campaign. 

Every click matters; every click costs money. But what if many of those clicks aren’t even from real users? How do you identify the clicks that you get on each campaign as genuine and from the right audience? 

Click fraud has emerged as one of the most complex, persistent threats in the world of digital advertising. It has now evolved into a sophisticated, multi-channel challenge that silently erodes campaign performance across both web and app ecosystems. From automated bots and click farms to SDK spoofing and device manipulation, fraudsters try to exploit every possible entry point of the campaign. 

And it’s not a niche problem. According to a latest bad bot report by Imperva, approximately 47% of online traffic is driven by bots, and notably, around 30% of these are harmful bots commonly linked to click fraud and other deceptive activities.

Moreover, according to an mFilterIt analysis of 196 campaigns, 17% of ad fraud was detected at the click stage in 2024.

Every click matters; every click costs money. But what if many of those clicks aren’t even from real users? How do you identify the clicks that you get on each campaign as genuine and from the right audience? 

Click fraud has emerged as one of the most complex, persistent threats in the world of digital advertising. It has now evolved into a sophisticated, multi-channel challenge that silently erodes campaign performance across both web and app ecosystems. From automated bots and click farms to SDK spoofing and device manipulation, fraudsters try to exploit every possible entry point of the campaign. 

And it’s not a niche problem. According to a latest bad bot report by Imperva, approximately 47% of online traffic is driven by bots, and notably, around 30% of these are harmful bots commonly linked to click fraud and other deceptive activities.

Moreover, according to an mFilterIt analysis of 196 campaigns, 17% of ad fraud was detected at the click stage in 2024.