Improve Mobile Click Fraud Detection with AI
IP Flagging not enough? Try AI-based fraud detection.
Fraudulent Clicks by IP
AI-based Click Fraud detection can help large advertisers save a lot of money that would otherwise go towards fraudulent ad networks and publishers.
Evolve with the fraudsters
Click Fraud is difficult to catch with simple techniques because the fraudsters are constantly evolving their approach. An AI-based detection system can evolve with the fraudsters and help you stay one step ahead.
Get data you can trust
Fraudulent clicks produce wrong click data, which can mislead your marketing efforts. Good fraud detection can filter out the bad traffic and make your analysis more meaningful.
Better than just IP Flagging
IP flagging is a good start, but sophisticated fraudsters hide between a network of IPs. AI can find the finer patterns and flag fraudsters even when they don’t overuse an IP, by also paying attention to device data, click time, and publisher channel.
Predict downloads and catch fraudsters
One good sign of a legitimate user is whether they download an app. You can train an AI model that learns to predict whether a user will download a mobile app after clicking an ad for it.
If the predicted chance of the user actually being interested in the app is extremely low, you flag the user as potentially fraudulent.
Input & output data
- App the ad is for
- Ad publisher
- Mobile device (iPhone7, Google Pixel, etc.)
- Time of the click
- Probability that a user who clicked on an ad will eventually download the app
Signature of a fraudster
The most important factors in deciding whether a click is fraudulent or not are (in descending order):
- Number of IP requests per device in an hour
- Ad publisher channel
- Number of requests for the same app from the same IP
- Number of requests from the same IP on a given day
- OS version of the mobile phone
- Number of requests from the same IP and OS in an hour
Need help with a machine learning challenge?Schedule a Call
OR send me a description to: firstname.lastname@example.org