Sophisticated Ad Fraud Can Be Tackled by AI: Appier
Appier, the artificial intelligence (AI) company, today shared the results of a real-world study that it conducted on its own network to demonstrate the ability of an AI-based model to fight ad fraud. Ad fraud refers to impressions, clicks or installs that are disguised as legitimate traffic.
Based on data over four months from May to August 2017 involving over 4 billion campaign data points including ad clicks and app installs, the report showed that an AI-based fraud detection model was able to identify twice as many fraudulent transactions as a traditional rule-based model.
The advantage of an AI-based model is its ability to pick out ad fraud patterns that are difficult for traditional models to detect. One example is what Appier calls “the chameleon”, where dishonest publishers appear to be legitimate publishers at first, only to generate fraudulent installs at a later date. Another suspicious activity detected by Appier’s AI technology is termed the “inventory burst”. With this method, a fraudulent publisher generates an abnormally high inventory count in the absence of an appropriate level of in-app registration activity.
“Ad fraud has become a major threat to the online advertising industry and is projected to cost advertisers billions of dollars,” said Joe Su, CTO, Appier. “Traditional rule-based methods of detecting and mitigating ad fraud has its limitations. Appier believes an AI-based model is far more effective and we are seeing the benefits of that approach in just four months of analysis from our network.”
A traditional rule-based model typically only looks at one to three dimensions and works on rules that are programmed by humans. Appier’s AI model works with simple and known ad fraud patterns, examining data along over 80 dimensions. It is also self-learning and thus able to identify suspicious patterns that it has not seen before.
“Just as with cyber fraud or financial fraud, ad fraud is becoming more sophisticated and is constantly evolving, so it’s important to be able to quickly identify and mitigate new threats. A traditional rule-based approach just cannot keep up with the fraudsters; an AI-based model is required to stay on top of evolving ad fraud patterns,” said Su.