It is essential to track and analyze specific user information, such as location, device type, IP address, browsing habits, and behavioral biometrics. By closely examining these data points, businesses can identify patterns that may indicate a high likelihood of fraudulent activity, such as the presence of bots or other automated processes. In addition to thoroughly analyzing user behavior and data points, machine learning is essential to process large amounts of data and learn from previous fraud incidents in real-time to uncover signs of fraud.
And, as mentioned earlier, mobile devices have become a more significant source of affiliate traffic and conversions. This means that online businesses need to adopt a mobile-focused fraud prevention strategy by implementing mobile-native solutions and using AI-powered tools to analyze and detect unusual patterns quickly and with high precision.
Affiliate marketing fraud can go unnoticed. We have the means to prevent it before it’s too late. Get in touch with us to see how we can help by clicking on 'book a call' at the top of this page. Alternatively, you can contact Marc directly via email at email@example.com or via LinkedIn.