Anomaly Detection in Traffic: Kayzen employs advanced features to identify unusual patterns or anomalies in traffic , such as irregularities in IP addresses, device IDs, and user behavior. By working with multiple third-party solutions, Kayzen enhances its ability to detect high volumes of traffic from single IPs or those flagged as proxies or VPNs, which are often indicative of bot activities.

Low-Quality Traffic: Kayzen actively removes Low-Quality Traffic across multiple exchange partners through curated Applists that are blocked at the platform level. Such traffic is typically identified through a combination of factors, including historical performance on other campaigns and indicators of IVT. Kayzen maintains a proprietary global block list of inventory flagged as possible candidates for IVT. This list is regularly updated and further validated by human oversight to ensure accuracy and effectiveness.

CPA Model Optimisation: Our CPA model is designed to optimise for post-install and purchase events, ensuring that we focus on high-value user actions that directly contribute to ROI. Unlike CPI models, which are vulnerable to fraud due to bots easily mimicking installs, our approach targets deeper, down-funnel events that are much harder for bots to replicate.

By prioritizing user behaviors such as in-app purchases or meaningful interactions after installation, we can more effectively filter out invalid traffic and concentrate on genuine, high-quality users who contribute to the success of our campaigns.

Click Signing with Appsflyer: This feature enhances the security of tracking URLs by ensuring that they cannot be tampered with or edited after being generated. By digitally signing each click, it prevents unauthorised modifications and invalidates duplicate clicks, making it an effective tool in combating invalid traffic (IVT). This process ensures that only legitimate, verified clicks are counted, significantly reducing the risk of click fraud and ensuring the integrity of campaign data.

High Rejection Rate Auto Blacklist: This feature leverages machine learning to identify and blacklist bundle IDs that exhibit patterns of fraudulent activity. This approach dynamically reacts to the real-time performance of your campaigns. Here, once a campaign is live, it continuously monitors and evaluates the quality of traffic based on post-install behaviors and rejection rates flags received from the MMPs. When an anomaly is detected—such as a spike in rejections for a specific bundle ID—the system automatically blacklists that bundle for that particular campaign , preventing further expenditure on potential invalid traffic(IVT).

Fraud Incident Response Strategy: A structured approach is employed when potential IVT is reported: