Advertising fraud is found everywhere on desktop and currently on mobile too. The fraudsters were influenced by significant ad budgets, pouring into mobile, as the result of which anti-measures were introduced. Then mobile-ad fraud evolved, and so did the counterattack, and so on.
Faking Impressions & Clicks
Initially it was easy with CPM and CPC pricing models. Fraudsters had to just fake an impression or a click. In the app economy, the CPI model is most common. This significant source of money drew their attention who masterminded multiple tactics to mimic an install.
Soon CPA model started to gain momentum as app economy became hyper competitive. Now loyal users became more important that new users companies started to focus on it. A CPA pricing model was a natural follow-up. Although more difficult to perpetrate (impersonating a click, install and in-app activity), in-app fraud is a real problem because its hefty payouts offer extra motivation to fraudsters to find ways to meet the technical challenge.
Prevent App Frauds
Prevention is more of a challenge because it has to take place in real time and prevent an install from being attributed to a fraudulent source. One way to overcome the real time challenge involves the use of what is known as deep learning (neural networks). These systems offer a newer class of highly accurate machine learning algorithms that train on hierarchical representations of input data.
In laymen’s terms, deep learning is about building a large set of simple functions that interact together to approximate a solution to a problem. A lot of the advancement in this field is around image processing (e.g. Microsoft Fetch which can identify dog breeds, Google deep dream etc.), but they can be applied in a wide variety of areas – fraud included. Models are trained on offline data, and then scoring based on a pre-calculated model is applied in real-time to enable fraud prevention. Obviously, the scale of data the engines train on to develop a model is a major factor in its effectiveness.
Fraud prevention is the responsibility of everyone in the ecosystem.Advertisers ought to run with esteemed ad networks, demand transparency from these networks, enhance their bi with anti-fraud specialists, and work with measuring partners which will supply sturdy anti-fraud capabilities.