learning and fraud prevention debunked
M(L)yth Busters 6 myths of machine learning and fraud prevention debunked
The typical approach to combating fraud is to look at all the different ways fraudsters operate and find some indicator based on their objectives. But every time one fraud tactic is identified, the fraudster evolves its tactics to evade detection. Increasingly, fraud resembles valid traffic. This article originally appeared on the TrafficGuard blog. Every time the fraudster finds a new vulnerability, valuable time is lost in defining the tactic and finding the rule to stop it.