AI Is Changing the Ad Fraud Fight

Ad fraud isn’t new to anyone running acquisition budgets across dozens or hundreds of locations. Multi-location marketers and agencies have dealt with click farms, bot traffic, and inflated app installs for years. What’s changing is the machinery behind the fraud. AI-driven ad fraud can now manufacture outcomes that mimic campaign success at a scale and level of sophistication that once would have taken a manually operated fraud operation years to build.

Branch recently surveyed 455 marketing and growth leaders across NA, EMEA, and APAC, and the results put numbers to what many marketers already sense. Eighty-seven percent say their concern about ad fraud has grown in the past year, while 73% report a noticeable increase in what they believe is AI-driven ad fraud.

AI-driven ad fraud is a serious problem in the industry. Respondents estimate losing 27% of their paid digital budgets to fraud annually, roughly $3 million a year for the average company surveyed. That figure tracks with Adweek’s reporting that fraud consumes roughly 20% to 30% of digital ad spend.

For MULO brands, digital fraud isn’t a single threat vector. It’s a little wasted spend in one market, a batch of questionable installs in another, a campaign in a third that looks like it’s high-performing but never turns into real customers. Add them all together and they become a material share of acquisition budgets.

AI is blurring the line between real and fake

Today’s AI-driven ad fraud — things like synthetic bot traffic that mimics real user behavior and AI-powered click and install spamming — is convincing enough that 83% of survey respondents say AI is making fraud harder to detect. Only 16% say AI is helping them fight back. Independent data backs up that trend line: One analysis of global programmatic traffic found invalid traffic rates more than doubled in 16 months.

Part of the problem is that many traditional fraud signals depend on behavior that AI has already learned to imitate.

Click-to-install time used to be one of the clearest tells: A near-instant install was a red flag, since a real person needs time to find, install, and open an app. Some studies have documented AI-driven networks staggering fake installs across realistic, varied time windows instead of generating volume alone so the behavior looks human.

As AI agents become more capable of navigating ad ecosystems, websites, and apps, distinguishing between “human-looking” and fraudulent behavior is only going to get harder.

Fraud detection is becoming a scale game

A system watching billions of events across many advertisers, geographies, and devices can spot a pattern that’s invisible inside any single campaign. A one-dollar purchase looks perfectly clean on its own; the identical behavior repeated thousands of times across unrelated accounts does not. Depth of data is now the difference between catching a scheme early and quietly paying for it all year.

It’s tempting to assume that the ad platforms with the most data will simply solve this for you. The largest players have invested heavily in fighting fraud, and it’s part of what’s made them so valuable to marketers. Marketers are leaning into that strength as one way to defend against fraud, moving away from open exchanges toward higher-quality, more accountable inventory — what one survey respondent described as a shift “from a volume-first approach to a quality-first, zero-trust strategy.”

But scale inside any single ad platform, however large, only sees that platform. Fraud doesn’t respect those boundaries. It moves across channels, campaigns, and markets, and the patterns that expose it often span more than one destination. That’s the case for measurement scale, and it matters most for multi-location brands and their agencies.

A neutral measurement layer that spans every channel and market gives you true visibility into fraud. A spike in one channel that looks legitimate on its own might turn out to be bot traffic once it’s compared against patterns everywhere else. Unified measurement complements what each platform reports by adding an independent, deduplicated, apples-to-apples view across a footprint no single partner can see end to end. That’s the difference between just having more data and getting better answers.

When you can’t verify the user, verify the outcome

That distinction is also the way through the agentic-AI problem. When a human, a bot, and an autonomous agent all behave identically, authenticating the actor becomes a losing game. The more durable defense is to not stop at top-of-funnel proxies like impressions or clicks, but to connect every signal through to the one thing Ai-driven ad fraud can’t fake: real, down-funnel outcomes. A fraudster can spoof a click, or even an install, but they can’t spoof a customer who pays you. Measurement that ties each upstream touch to real results is what makes the whole chain trustworthy.

For multi-location marketers, that means holding every market and every vendor in the chain to the same standard — verified performance through to purchase, measured the same way everywhere — rather than piecing together different measures of success in each market. It’s the difference between “this campaign reported conversions” and “this campaign produced customers,” applied consistently across the whole footprint.

None of this means retreating from new channels. New channels keep creating new ways to reach customers, and fraudsters will always follow the money there. But as AI blurs the line between real and fake, and between human and agent, marketers have less room than ever for black-box measurement. The winners will be the ones who insist on independent, consistent measurement across every market they run in, and who judge success by outcomes no bot can fake.