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The Battle Against Fraud in Move-to-Earn Apps

Creating a move-to-earn app seems like a smart choice these days. The number of move-to-earn apps is still not huge, so it’s not too saturated. While there are some big competitors like Sweatcoin, Walken, and a few others, the demand from the side of users is probably still not met, as most apps only reward walking, not fitness.

Let’s take a look at Walken — the crypto app that rewards walking. They surpassed 1 million registered users in August 2022, and just one quarter later, they reached 2 million users, which is pretty incredible. The quick growth of users indicates one — it’s still a new and exciting thing for many people. Whether you pay out dollars, discount coupons at your partners’ retail places, or even cryptocurrency, people are always interested in earning something from doing regular activities.

However, it’s not all sunshine and rainbows. While it might be relatively easier to hook the users with rewards, some of them will definitely look for ways to get the most out of it, even if it means cheating. There are multiple videos on YouTube, dedicated Reddit pages, and even articles on how to trick the move-to-earn apps into thinking users did the activity, although they didn’t even lift their sitting parts from the computer. This is where the fraud detection tools come in. In this blog post, we’ll review the most popular fraud detection tools and review their benefits and drawbacks.

Ways to Detect Fraud in Move-to-Earn Apps

Now that we understand the potential for cheating in move-to-earn apps, let’s dive into the various methods used to detect and prevent fraud. Move-to-earn apps employ a range of strategies and technologies to ensure the accuracy and reliability of their platforms. Let’s explore some of the most common fraud detection methods:

1. GPS Tracking: Many move-to-earn apps, especially those that count steps and distance, rely on GPS tracking. This technology uses your smartphone’s GPS capabilities to track your movement and calculate the distance covered. By analyzing the GPS data, these apps can verify whether users are actually moving or simply faking their activity.

The benefits of GPS tracking are clear — it provides precise location data and enables accurate measurement of distance covered. However, it does have its drawbacks. For instance, indoor activities or areas with poor GPS signals can sometimes result in inaccurate tracking. Additionally, GPS alone cannot detect fraudulent activities, as users could just throw their phones into a car (or even attach them to their furry friends, as one page suggests), and the tracker would count that activity. And, of course, if your app is focused on fitness, GPS tracking is not the right solution for you. But read on, as we’ll cover a few better-suited ways to track your fitness activities.

2. Motion Sensors and Heart Rate Monitors: Other move-to-earn apps take advantage of motion sensors, like accelerometers and gyroscopes, as well as heart rate monitors. These sensors can capture detailed data about your body movements, activity intensity, and heart rate. Apps can verify your claimed activity level by analyzing this data.

The use of motion sensors and heart rate monitors offers several benefits. They provide more accurate insights into the user’s activity levels and can differentiate between genuine movements and artificial ones. However, these methods may require additional hardware, such as wearable devices, which might not be accessible to all users. In fact, as of 2020, only 30% of adult Americans used wearables for healthcare. Though this number has gone up in the past few years, it definitely restrains the move-to-earn apps that only want to rely on this method — the potential user base shrinks to a significantly lower number of users.

Moreover, only relying on a gyroscope or accelerometer on your app would also not give you the desired results — a simple move such as shaking the phone to simulate movement could easily deceive the gyroscope into thinking that the user is actually exercising. So, in this case, GPS tracking is necessary.

3. Manual Verification (People Uploading Pictures or Videos of Them Exercising): Some move-to-earn apps employ manual verification as a fraud detection method. Users are required to upload pictures or videos as proof of their activities. This allows app administrators or community members to manually review and validate the authenticity of the user’s efforts.

Manual verification has its advantages, as it involves human judgment and can detect sophisticated forms of fraud. However, it can be time-consuming for both users and reviewers, potentially leading to delays in verifying and rewarding activities. Moreover, it may not scale well for apps with a large user base, as it requires employees working full-time just to review the videos. So although this might be a temporary option when you’re just starting out and don’t have funds for other technology, it is definitely a no-go for apps that see high user growth.

4. Building Machine Learning Algorithms: Move-to-earn apps are increasingly leveraging machine learning algorithms to detect fraudulent activities. These algorithms are trained on vast amounts of data, including motion tracking information, to classify and understand whether users are actually exercising or simply faking it.

The benefits of machine learning algorithms are notable. They can analyze patterns, detect anomalies, and continuously improve their fraud detection capabilities over time. However, it’s important to mention that training these algorithms requires substantial manual data labeling, which can be labor-intensive and time-consuming. That’s why this method is usually only accessible for big apps with plenty of resources.

5. Off-the-shelf Motion Tracking Technology: Another innovative approach to fraud detection in move-to-earn apps is through advanced motion tracking technology. This technology can accurately capture and analyze users’ movements in real time by utilizing sophisticated motion tracking and algorithms that scan their bodies straight from their phone camera or webcam.

While it might not be a fit for apps that only offer rewards for walking, it’s probably the best fit for all apps wanting to reward users for fitness — it provides detailed and precise insights into users’ exercise techniques and form, enabling real-time feedback and virtual coaching. Moreover, it minimizes the need for extensive manual data labeling compared to machine learning algorithms, allowing quicker implementation and updates. Another benefit is the implementation — multiple companies are offering ready-to-use solutions that take up to a few weeks to implement.

In conclusion, the problem of fake fitness in move-to-earn apps is a real challenge that can undermine the accuracy and trustworthiness of these platforms. However, new technology offers promising solutions to detect and prevent fraudulent activities.

As move-to-earn app developers and enthusiasts, it’s crucial to prioritize the integrity of your platform. Consider incorporating motion-tracking technology to provide accurate and reliable feedback to your users. Not only does it help detect fake fitness, but it also opens up opportunities for real-time feedback and virtual coaching, enhancing the overall user experience.

So why not give motion tracking technology a try? You can see firsthand how it works by exploring our demo here:

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