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08 February 2022
08 February 2022
Posted by TrafficGuard

The War on Ad Fraud: Why Machine Learning Offers The Best Line of Defence

The fight is real, and it’s draining your budget and campaign performance.

As fraudulent tactics become increasingly sophisticated, the battle against ad fraud is an uphill one. With an arsenal of effective methods, like domain spoofing, ad stacking, and pixel stuffing, fraudsters present a genuine threat to healthy conversion rates and optimised ad spend. Over the next three years, advertisers are set to lose a predicted $100 million a day to ad fraud, with more than 20% of ad transactions coming from fraudulent sources.

You may view your competitors as your biggest roadblock to success, but ad fraudsters present a uniquely difficult enemy.


Battle metaphors aside, the fight against fraudsters gets stronger everyday. The use of machine learning in ad fraud has increased hugely in the past few years, with artificial intelligence presenting itself as a useful tool for the rapid and efficient analysis of large quantities of data.


Let’s take a look at how the battle against ad fraud is going. What happens if we ignore the threat of ad fraud? What are fellow comrades doing to combat ad fraud? And what can you do to mitigate the threat in your own organisation?


What can happen if you don’t combat ad fraud?

If ignored, the consequences of ad fraud can be huge on budgets and performance. Reducing invalid traffic (IVT), especially the malicious kind, should be a priority for marketing teams as it wreaks havoc on three very important metrics; ROI, conversions, data validity.

Your ROI will not be optimal - Large amounts of IVT can be incredibly detrimental to ROI. A customer of ours recently found that 28% of their ad spend was going towards invalidated clicks, amounting to a massive $65K wasted budget.

Your conversion rate will be low - As well as compromising campaign efficacy, large volumes of IVT can cause marketers to direct spend to traffic sources which appear lucrative, but are in fact producing non-opportunities. So your click-through rate may be high, but as IVT is unlikely to lead to genuine sales opportunities, your conversion rate will be way down low.

Your data validity will be compromised - Without complete certainty in the validity of traffic, marketers may struggle to make efficient campaign optimisation decisions, and any changes they do make that are influenced by IVT could be detrimental to campaign success. In the world of digital marketing, data is gold—so the cleaner and more accurate you can make your data, the better your campaigns will be.

How is the industry using machine learning to combat ad fraud?

A subset of artificial intelligence, machine learning (ML) extracts patterns and relationships from data and expresses them as a formula that can be applied to new data sets. Over time, as the data changes, new patterns are learned by the model without the need to explicitly program them. Because of the scale of data processed, insights can be more valuable and derived much faster using machine learning than by using human analysis alone.

Widely used in the cyber security space already, ad tech seems to only just be catching on to the value of ML in ad fraud prevention. Juniper Research forecasts that by 2022, machine learning could save advertisers over $10 billion a year in ad spend that would have been wasted on fraud.


Fraud prevention solutions which utilise ML are capable of analysing the volumes of data required to predict the likelihood of fraud in real-time. The speed, efficiency and accuracy of these solutions means they are able to handle the vast amounts of data that need to be processed to detect fraudulent activities.

ML also gives fraud tools the ability to detect anomalies, perform predictive modelling and find clusters to identify new and earlier indicators of fraud. Because the efficiency of fraud detection and mitigation from these services will increase as ML is fed more data, it gets better and more intelligent everyday.

“If you’re developing a marketing strategy or budget, considering ML is an absolute must.”

Matt Sutton, Global CRO at TrafficGuard

How is TrafficGuard fighting ad fraud?

Our ad fraud prevention platform analyses traffic at multiple points in the advertising journey including the click level, the attribution level and post-attribution level. This enables us to block IVT as soon as it is detected and also find earlier indicators of tactics that are conventionally diagnosed at the attribution level. Specialising in ML-driven fraud prevention, as opposed to just detection, we limit the impact of fraud by ensuring performance data stays clean and fraud is blocked in real-time.

With the effects of ad fraud mitigated, marketers can use their cleansed data to make bold, informed decisions throughout all of their campaigns.


Forward-thinking enterprises who wish to layer AI protection across all their marketing campaigns can reach out to the TrafficGuard team here.