The rise of mobile ad fraud indicates how important mobile devices are to us. Millions of consumers across the globe engage with mobile apps every day, so much so that the growth of the market is expected to be 14% annually from 2024-2030. Since the app market is growing marketing of these apps is also growing and a lot of it is through affiliates. With digital ad spend comes wastage due to ad fraud.
As per Fortune Business Insights, the size of the mobile advertising market is at $175.62 billion in 2023 and is expected to grow to $1,040.08 billion by 2032, with an annual growth rate of 21.8% (2024-2032).
Though affiliates deliver the expected performance, they bring with them an ad fraud rate in the range of 20-40% (Source: mFilterIt Reports).
What is Mobile Ad Fraud?
Mobile ad fraud encompasses a set of techniques employed by fraud via various mobile devices such as smartphones and tablets and tablets. It deliberately serves mobile ads that have no potential of being viewed by real users, thus artificially inflating the number of ads served.
These fraudulent activities include fake app installations, fake clicks and even false app purchases.
Types of mobile ad fraud
- SDK Spoofing – It stands for Software Development Kit. SDK spoofing creates legit-looking installs by using data from real devices without any actual installs thus consuming all ad budget.
- Bundle ID Spoofing – Under this technique, the fraudsters trick advertisers by making them believe that their ads are running on a specific app, but they might be running on a different app. This is done by changing the parameters of the second app to a fake identifier, thereby creating a bundle ID spoofing.
- Bots and Emulators – For a mobile advertisement ad fraud, an emulator (software that can be run on any device to host devices to simulate tasks) can fake a device to look like a smartphone and then perform fake app installs, and impressions without using the real device.
- Click Flooding – Here fraudsters aim to take an impression of the last click engagement before the actual install. This is done by sending fake clicks in large numbers with the intent of getting credit for the download.
- Organic poaching – Here an advertising channel claims for an app install that could have happened organically. The goal is to change the actual installation statistics to make a particular XYZ channel more effective than it is.
Why MMPs are not enough for Ad Fraud Detection
Imagine you are running a mobile ad campaign promoting your app across many ad networks. Your brand aims to attract real users to install and use your app. Your brand approached an attribution platform to help maximize your campaign reach and track its optimization.
But that is not the case in traffic validation as there are various shortcomings that attribution platforms lack like the accuracy & correctness of the validation is further fueled through enrichment using the repository of data signals. This helps analyze behaviour and evaluate how it links at a higher level to deliver a better risk assessment.
The bigger issue is also of conflict of interest. Attribution platforms end up getting paid on the attributions they record. The conflict arises because they do not get paid for the attributions marked as fraud which impedes the source of their primary earnings. Hence, most attribution platforms provide ad fraud detection services to make advertisers contend that they are protected against ad fraud.
Moreover, the depth of the ad fraud detection services provided by attribution platforms is also shallow. They lack the checks and ML-based algorithms to detect new and sophisticated types of fraud.
The fraud detection mechanism of attribution platforms does not have full-funnel detection capabilities. With no or minimal depth in fraud detection. By the time fraud is detected Ad spend already been wasted by fraudsters.
Prevention against mobile ad fraud
Various preventive measures can be adopted against mobile ad fraud as:
- Deploy third-party individual ad traffic validation tools which can help with down-the-funnel analysis
- These tools should be enabled with AI-ML tech to provide real-time analysis and optimize channel management
- Advertisers must look at their data. In a lot of cases, simple signals are visible which may indicate ad fraud
Marketers struggle to combat this threat; the preventive measures should be powerful enough to deal with it as the tactics of fraudsters have become even more complex. Here, innovative ad fraud solutions like mFilterIt come into the picture with its full funnel protection which goes beyond the limitations of attribution platforms.
It analyzes data from multiple layers across all stages of the user journey, from installation to in-app engagement through sophisticated AI-ML to identify fraudulent patterns in real-time. Ensuring genuine user actions are attributed to your mobile Ad spend.
Conclusion
As mobile ad fraud continues to grow and evolve be it biddable, non-biddable, programmatic, affiliate marketing, or any other kind of approach a market adopts ad fraud can happen in all of them so brands need to look beyond the dollar impact of ad fraud and be aware of how ad fraud is reversing the customer experience, which is the essence of any digital transformation endeavour. While many of the attribution platforms claim to offer fraud detection services their ability to do it is often limited.
