The Power of Machine Learning to Fight Fraud

A fraud detection product should be able to look at the traffic from multiple angles to cover as much of the attack surface as possible. For example, simple tricks like looking at the request velocity coming from clients works with volumetric and simple attacks but attackers have learned to circumvent this by load-balancing their traffic through proxy services long ago. A ruleset can help detect signals typically related to fraudulent activity, but the more advanced fraudsters over the years have refined their strategies, making such a ruleset less effective, especially since it may not be updated fast enough. The detection layer must consider multiple signals and have algorithms to automatically recognize anomalies and score the traffic accordingly.