Fraudulent devices and app churn are becoming a growing challenge in today’s digital world. Fraudsters often use manipulated or fake devices to deceive businesses and drain marketing budgets. These fraudulent activities not only distort user data but also contribute to higher app churn rates.
Genuine users who feel overwhelmed by the degraded experience or security risks, may be driven away, exacerbating retention problems. Without effective detection and prevention strategies, companies risk making misguided decisions that ultimately damage their reputation. Therefore, businesses urgently need the robust anti-fraud solutions to prevent it.
What Are Fraudulent Devices?
Fraudulent device means using compromised mobile devices to manipulate any activities to mimic the real user behavior. These activities are done by fraudsters to fake app installs, engagement metrics, and even in-app purchases, including exploiting advertising budgets or skew analytics.
Unlike legitimate users, fraudulent devices don’t provide real value to businesses because they exist purely to deceive and extract financial gain. This false activity can lead to wasted marketing spend, corrupted user data, increased churn rate, and damage to an app’s reputation.
Types of Fraudulent Devices
There are various types of fraudulent devices used to carry out mobile fraud, each with its methods of deception. Each also poses unique challenges for app developers and marketers trying to protect their platforms. Businesses need to be able to identify and block these fraudulent devices to maintain trust and grow their user base authentically.
1. Emulators
This computer software tool that can be easily reset and reconfigured, and they are notoriously capable of impersonating thousands of “new” devices to fake user profiles and activities that seem legitimate at first glance.
Fraudsters prefer emulators because they are cheaper, faster and scalable. Instead of using physical phones, fraudsters run apps inside these virtual environments to fake installs, clicks, and engagement at a massive scale. They can inflate acquisition costs, skew analytics, and harm an app’s reputation by filling it with non-human users.
However, emulators often leave subtle clues that can help the detection system to spot them. For example: repetitive behavior patterns, unusual operating system signatures, or missing hardware sensors.
2. Spoofed Devices
As real or virtual devices, spoofed devices are devices that have been intentionally modified to fake identifying information. This spoofing technique is used by fraudsters to change IP address, device ID, GPS location, or even the device model and operating system version. Therefore, one device can impersonate many different users and be able to do fraudulent activities that are harder to detect.
Spoofed devices are often used to bypass location-based targeting, exploit geographic-specific promotions, and appear like fresh users. Those activities can be done because they mimic real user behavior but operate under false identities.
To be able to detect spoofed devices, we should be looking for inconsistencies between device signals, location anomalies, or behavior patterns that don’t match the typical user habits.
3. Rooted Phones
Rooted phones are mobile devices that have their built-in security protections removed to give the user full administrative access that usually called root access. Fraudsters are using it to bypass app security, modify app behavior, or hide malicious activity.
A rooted phone can fake clicks, fake installs, or in-app purchases, and even manipulate add tracking systems. Some fraudsters use it to install apps multiple times, reset device identifiers, or simulate real use engagement. These fraud activities are extremely difficult to detect at surface-level data.
Rooting is not always malicious as some tech-savvy users root their phones for customization. However, in the context of app fraud, rooted devices are a serious red flag because they open access to large-scale abuse if not stopped immediately.
4. Device Farms
The term farms here are large collections of real and physical mobile devices that are controlled by fraudsters to generate fake user activity at scale. They could use a lot of mobile devices, such as phones and tablets, to perform app installs, ad views, clicks, or in-app engagements.
Device farms can be run fully automated by using specialized software that manages multiple devices at once. Some even rent out their services to inflate their user base deceptively. By using real hardware and networks, device farms are even harder to detect due to their similarity to the real user behavior.
However, some things can expose them, such as patterns like repetitive actions, unrealistic engagement timing, or identical device setups.
The Impact of Fraudulent Devices on App Performance
Fraudulent devices can give a huge impact on the performance of an app. When fraudsters as fake users generated by any type of fraudulent device, they distort key performance metrics like installs, engagement rates, and retention. This activity also drives up mobile app churn rate, as fake users typically uninstall quickly which skews lifetime value (LTV) calculations.
This false data leads companies to make poor marketing decisions and could waste budgets on acquiring “users” who never deliver real value. Beyond the financial loss, fraudulent devices can damage an app’s reputation and affect store rankings, trigger scrutiny from platforms like Google Play and the like.
Fraudulent Devices and Their Role in User Churn
Fraudulent devices are operated by bots rather than real users, their interactions with the app are shallow, short-lived, and meaningless. After faking installs or making initial engagements, they usually “drop off” immediately to create the illusion of users who abandon the app shortly after joining.
This artificial mobile app churn inflates churn rates, lower retention metrics, and makes it difficult for businesses to understand and improve the actual user experience. To make it worse, this fraudulent activity can create a poor in-app environment that potentially leads to the real users to leave as well.
Over time, the combination of fake churn and frustrated real users can severely harm an app’s growth, reputation, and profitability.
How Keypaz Detects and Blocks Fraudulent Devices
When a suspicious device is detected, Keypaz can instantly block it from accessing the app. Keypaz uses advanced detection methods to identify and block fraudulent devices before they can harm app performance. Using a wide range of device signals, Keypaz will be able to spot inconsistencies that reveal them.
Machine learning models will continuously study real user behavior to detect the signs of fraud, such as unusual interaction patterns, location spoofing, and rapid device resets. It also analyzes device signals like hardware fingerprints, network patterns, and sensor behavior.
By providing detailed fraud analytics, Keypaz can help businesses understand attack patterns and fine-tune their defenses. Therefore, businesses will be able to make smarter marketing decisions based on clean and reliable data. Protect your business and grow it together with Keypaz now!