The merit of ML-based anti-fraud solutions is evident when it comes to specific metrics. Consider the time-ordered distribution of user behavior, for example. Let’s take the cohort of downloads where all hardware metrics are genuine - i.e., a real device with a unique device ID, IP, and TTI that mimics real user behavior. A rules-based solution will define such installs as non-fraudulent by default. At the same time, the analysis of the same cohort with the ML-based solution would reveal anomalies in the time it takes a user to reach the next level within the game.
This example follows the actual experience of one of Scalarr’s customers - a major game developer, who was using our service alongside a rules-based solution. The anomalies in the app’s traffic were detected on the post-install level: while “human” users take anywhere between five and ten minutes to progress to a certain level in the game, in a fraudulent cluster this level was achieved within just twenty to forty seconds. The ML algorithm identified the discrepancy and marked installs as fraudulent, despite hardware metrics looking completely real.
Here’s why a rules-based solution was unable to provide the desired outcome:
- First things first, all hardware metrics were perfectly forged by fraudsters.
- The setup of the “behavioral” rule ( time to reaching a certain level) requires having the “right” parameters, which in this case can vary over a wide range for each particular install.
By contrast, a Machine Learning algorithm analyzes data by breaking it into cohorts (or clusters) and compares every single cohort against that of real user behavior. It then calculates the probability and deviation degree to identify significant anomalies and detect fraudulent traffic.
Ultimately, ML-based anti-fraud solutions are capable of providing well-rounded protection against well-known and emerging types of app install fraud. Thus, producing clear outputs with actionable data, personalized for each specific app.