Ad fraud is not a new challenge, but it is one that continues to evolve, driven by sophisticated tactics and increasingly automated schemes. With costs related to digital advertising fraud worldwide estimated to almost double from $88 billion in 2023 to $172 billion in 2028, this is clearly an issue that is not going to go away on its own. With the rise of artificial intelligence (AI), fraudsters have been exploiting advanced technologies to scale their operations – creating new risks for marketers and the ad tech ecosystem. Yet, there is also a bright side. AI is leading to the creation of the tools needed to fight back. Reshaping fraud prevention strategies to keep pace with these threats.
While some fraud-fighting efforts focus on law enforcement or rule enforcement, the most effective deterrent remains economic. Making fraud too difficult and unprofitable to sustain at scale. This framework begins by dismantling financial incentives by focusing on the top of the funnel, and builds on smarter, faster detection systems that adapt as fraud evolves. Here is how it works.
Provide an economic checkpoint
Effective fraud protection requires precision. By focusing on the measurement layer where ad performance and conversions are recorded fraudulent activities can be identified before they impact metrics or budgets. This approach ensures that only valid, high-quality interactions are included in attribution, allowing marketers to maintain trust in their data.
Fraud prevention at the ad delivery and measurement stages serves distinct purposes within the value chain. While fraud may infiltrate campaigns at either level, the measurement layer can act as a pivotal checkpoint to identify and isolate fraudulent interactions before they distort metrics or impact budgets.
Prevention versus remediation
Not all fraud is created equally. While some activity can be caught after the fact, the most effective fraud prevention focuses on blocking bad actors early before fraudulent traffic can be counted. Pre-install fraud detection stops fraudsters in their tracks. Ensuring illegitimate impressions or clicks are excluded from attribution before they can distort campaign performance.
Post-event analysis still has its place, especially for detecting patterns over time, such as anomalies in lifetime value or repetitive behaviour across multiple campaigns. However, prioritising pre-install protections reduces reliance on retroactive corrections, resulting in cleaner data and less waste for marketers. It also removes the friction caused by post-campaign remediations.
Establishing a more nuanced baseline
Traditional attribution methods rely heavily on quantitative signals like impressions or clicks. However, these are easy targets for fraud. To address this, fraud systems should establish a more nuanced baseline of authentic engagement. One that considers qualitative factors and behavioural cues to separate the real from the fake.
For instance, real interactions often involve active participation, such as engaging with an ad, completing meaningful actions, or navigating further into an app or site. By capturing these deeper signals, fraud prevention systems can establish a baseline for authentic behaviour, making it easier to expose fraudulent activities.
Implement AI for real-time detection and response
Fraud evolves rapidly, and so must the systems designed to combat it. Real-time detection, powered by AI, allows marketers to identify and address fraudulent activity within hours rather than days.
By analysing vast amounts of data using probabilistic models, such as Bayesian algorithms, AI can detect patterns and anomalies significantly faster than human analysts. Once fraud is identified, countermeasures can be deployed immediately, ensuring minimal campaign disruption and reducing financial losses.
Continuous learning and adaptation
Fraud is a cat-and-mouse game. As fraudsters develop new methods, detection systems must continuously adapt. Modern fraud prevention systems use AI not only to identify fraud but to evolve their algorithms as trends emerge.
This iterative process ensures protection systems stay one step ahead, refining their ability to differentiate between legitimate and fraudulent interactions over time. While AI drives the process, human oversight remains essential to validate and guide these evolving models.
Building an ecosystem that disincentivises fraud
Of course, the fight against fraud does not happen in isolation. Integrating these strategies into the broader ad tech ecosystem strengthens the integrity of the entire funnel. Protecting the measurement layer and incorporating upstream solutions ensures fraudulent activity is stopped before it can distort attribution or waste marketing spend. Protecting the top of the funnel in this way creates a unified front against bad actors and reduces the resources required for remediation.
Although this framework was developed primarily for mobile advertising, its principles apply across other channels. Connected TV (CTV), retail media networks (RMNs), and similar ecosystems face parallel challenges. Adopting these methods helps disincentivise fraud and build a more transparent, trustworthy and resilient supply chain.
By modernising fraud prevention strategies with AI the industry can make fraud unprofitable, unsustainable, and ultimately less prevalent. This, in turn, would ensure marketers are empowered to explore new inventory sources with confidence, test fresh audience segments, and pursue lower CAC and improved ROAS without fear of compromised results.
