In a world where every business tenuously related to digital advertising claims they can offer some type of fraud protection, it is important to know how to cut through the noise to reach the facts.

This article originally appeared on the TrafficGuard blog.

One of these key facts is that if you are getting fraud mitigation or detection from a non-specialist, their core function is probably dictating how comprehensive the protection they provide is. In most cases, they will only have visibility over a single point in the user journey – the click or the impression or install. But that is only one piece of the puzzle. To get the full picture of valid and invalid traffic, you need all the puzzle pieces.

Here are three important reasons why multi-point analysis is essential to effective fraud mitigation:

  • As fraud becomes increasingly sophisticated, more and more tactics can evade detection at a single point. Some tactics can’t be detected at the impression level, and only become apparent at later stages in the journey. So should you get a one-trick pony for each level? No! Multiple solutions looking at different levels can’t share information efficiently. One solution operating across all levels is the only way to corroborate details across different points and find earlier indicators.
  • You need to be able to compare the traffic at different stages for multi-point corroboration required to identify SIVT. For example, one indicator of fraud is the time between the click and install events occurring. If a fraud tool only looks at the install, it can’t determine if the click to install times are anomalous and will miss invalid traffic. Scoring based fraud mitigation relies on a score profile that builds with every engagement of the bot or user. This means when traffic is invalidated, it is not based on one indicator at one level but on as many as 200 that accumulate over the whole journey. Therefore, multi-point corroboration results in much faster and more reliable mitigation of both fraud and false positives.

  • With multi-point analysis, machine learning can be applied to find earlier indicators of fraud tactics that have previously remained undetected until the install or post-install. For example, we might see a high volume of invalid traffic blocked at the attribution with similar profiles. If we can also see earlier stages in the journey, it is possible to identify new, earlier indicators of that type of traffic to potentially block it sooner.

So why would any fraud prevention only offer detection at one level of the user journey?

Solutions that only detect IVT at one level have typically been developed to perform an alternative function at that one specific stage in the user journey, be it impression or click or install. As awareness of ad fraud and the demand for mitigation solutions has grown, they have started offering fraud protection on top of that related core function, using their existing technology. To provide multi-point analysis that is essential to combat sophisticated fraud, fraud prevention technology needs to be built from the ground up.

To learn more about addressing ad fraud through multipoint analysis and machine learning, download our whitepaper.