Ad fraud and invalid traffic continue to be an issue in the digital advertising ecosystem. However, with the use of Artificial Intelligence and Machine Learning, combined with strong partnerships with customers and exchanges, we have a suite of strategies and tools to effectively identify suspicious patterns and protect against fraudulent activities in order to reach genuine audiences.
Pre-Bid
Detecting Suspicious Traffic
We use Maxmind's bot IP filtering to remove or prevent proxy, datacenter, or VPN IPs from bidding. We address the pre-bid process by detecting anomalies such as abnormally high click-through rates or patterns typical of bots and click farms.
Detecting Low-Quality Traffic
Low-quality traffic is filtered out using curated block lists, which are built by detecting invalid traffic through historical performance and fraud indicators. Machine-learning algorithms analyze bid requests, impressions, and key metrics to spot anomalies in behavior, device IDs, and IP addresses. Regular updates and human oversight maintain accuracy and effectiveness.
Post-Bid
Auto Blacklisting
Machine-learning is utilized to dynamically identify and blacklist bundle IDs with fraudulent patterns. By analyzing real-time campaign performance, post-install behaviors, and rejection rate flags from MMPs, it detects anomalies like sudden rejection spikes and automatically blocks suspicious bundles, preventing wasted spend on bot traffic.
Click-Signing
Ad fraud prevention is made more robust by securing tracking URLs with digital signatures, preventing tampering or unauthorized edits. Each click is verified, invalidating duplicates and ensuring only legitimate clicks are counted. This reduces invalid traffic (IVT), mitigates click fraud, and preserves the integrity of campaign data.
Manual Methods
Despite of all the above listed automation in place, fraud attacks can happen 24/7 and fraudsters may, unfortunately, always be a step ahead. We take ad fraud very seriously - here are some other ways to thwart it manually.
Manual IP Blacklist
Include {USER_IP} macro in our click URLs so that you can start tracking the user IPs present in the ad request. This is usually different from the IP address tracked by the tracking tools. For example, we can collect and segment these user IPs into a blacklist of bad IPs which bring only landing page visits but with no further actions for app-to-web campaigns. This exercise can be done on a weekly basis.
Manual Placement ID Blacklist
Blacklist the placement IDs which show high suspicious visits and/or clicks as per 3rd party reports. This can be done on a daily/weekly basis as well.
Effective ad fraud management in programmatic advertising combines advanced technologies like machine learning, real-time monitoring, and collaborative tools to detect and prevent invalid traffic. By proactively identifying fraudulent patterns, blacklisting suspicious sources, and securing campaign data, advertisers can ensure better ROI and maintain trust in the ecosystem. A continuous, adaptive approach is essential to staying ahead of evolving fraud tactics.