Fraud in mobile advertising – find and deactivate

With the development of the digital data market, the quality of purchased traffic becomes more urgent. To reach broader audience segments, brands are ready to wade through the jungle of programmatic ads, encountering hundreds of specialized platforms that promise unique opportunities.

Online fraud is a swindling business model aiming to prompt the user to do some action (clicking a link with a disseminating malicious software, sharing information, installing malware, etc.) to get some profits by doing this action. Fraud in advertising is connected with the artificial promotion of clicks, impressions, conversions to obtain benefits. 

Managing The Risks Of Mobile Fraud

According to a Future Digital Advertising Study on Artificial Intelligence & Advertising Fraud of 2019-2023, fraudulent traffic will make advertisers lose about $100 billion over four years. 

During the quarantine, chat apps’ popularity skyrocketed, leading to a 12% increase in fraud in this segment in the United States. In some verticals, such as the entertainment segment, marketers could cope with this increase in popularity by protecting themselves from fraud. During the entire pandemic period in the United States, this segment’s fraud rate did not exceed 4%. The pandemic will undoubtedly change a lot globally. Still, it is already clear that the field of mobile advertising is quite capable, even in such conditions, to successfully fight against fraud and reduce its level in the market.

For example, Machine learning for fraud detection is actively used to find sophisticated fraud traits that humans simply cannot detect.

Ad fraud methods aren’t staying still. They evolve and adapt to anti-fraud solutions — bypassing install fraud detection logic. Furthermore, fraudulent publishers often heed towards more profitable targets implementing new methods to evade standard install fraud detection systems.

Let’s overview some of the most prevalent types of fraud in mobile advertising.

SDK Spoofing

Fraudsters embed data transmission between the tracking SDK in the application and the server receiving the information (usually through a man-in-the-middle attack) and then make bogus installations or other actions. Since all transmitted data is open to them, they can see what data is sent by the SDK during specific actions within applications (opening, passing a level, making purchases) and, thus, fully or partially simulating real users’ work.

Click Spamming

It is a type of mobile fraud where some publisher sends clicks, hoping that one will be the last one and counted by the system as the application installation initiator. Incorrect attribution of installs occurs: the source appropriates installs from other sites. If the app has good organic traffic, this type of scam is exceptionally effective.

Click Injection

It is a type of mobile fraud involving disruptive software installed on the user’s device and works by “intercepting” the user’s last click when downloading the application. Using an infected application in the system, the scammers see that the installation of a new application has started on the device. After it launches, they fake a click on an ad and attribute the usual organic installation to themselves, getting paid for it.

Such a scheme drains the marketer’s budget and directly affects conversion rates, misleading you about the real success of various advertising channels. Marketers and companies are responsible for falling into this vicious circle as they may pour more and more money into fraudulent ads that don’t generate installs.

 Bots and Emulators

Bots can simulate whatever activity like installs or clicks. They do not bring real traffic, but the advertiser may think that he is buying real customers. Such “users” can be identified by low retention and too high CR, and the absence of real purchases. Moreover, in their actions and retention-activity, repetitive patterns are often visible, by which they can be revealed. You can prevent such fraud by analyzing the geo-activity of users. For example, if most of them are different from your target location. The same is with the analysis of installations by characteristics and names of devices.

Device Farms

These types of mobile ad fraud use real or simulated mobile devices to click on ads and convert them into installs. 

There are two types of such farms: pre-programmed device farms that require automatic actions in the emulating hardware and manually-powered device farms.

You can prevent fraud from bots farms by analyzing the geo-activity of users. For example, if most of them are different from your target country. The same is with the analysis of installations by characteristics and names of devices.

While there are plenty of approaches to preventing fraud, businesses also use AI principles parallel to complex probabilistic rules. They are constantly working toward embracing new technologies and processes to enhance mobile ad fraud prevention systems’ functionality. 

Top-Notch Ad Fraud Prevention Tools


The program can detect, moderate, and notify about digital ad fraud. It analyzes your traffic and website visitors by breaking traffic reports into meaningful segments or channels. TrafficGuard analyses ad campaigns’ overall activity to alleviate ad fraud at its earliest pervasion. Adding a proactive strategy will keep the performance data clean and assists in optimizing the advertising campaign.


The platform provides such solutions as measurement, fraud prevention, cybersecurity, marketing automation, and tieing the desktop and mobile data. It makes marketing simpler, smarter, and more secure for thousands of apps. The platform assesses traffic patterns to drive optimization and provides comprehensive ad fraud prevention that proactively stops fraud before it happens while you can focus on your business.


It is an anti-fraud solutions developer that assesses the quality of mobile and web traffic in advertising campaigns. It offers anti-fraud solutions on all levels of the ad campaign funnel. FraudScore’s algorithms consider more than 150 indicators to comprehensively analyze incoming traffic and detect suspicious fraudulent schemes in time. This approach allows FraudScore to fight both well-known and new types of fraud effectively.

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