We hear it all the time: Fraud prevention is hard because fraudsters continually change and adapt. The minute you figure out how to recognize and prevent one scam, a new one emerges to take its place.
Naturally, then, the best technology for fighting fraud is one that can change and adapt as quickly as the fraudster’s tactics. That’s what makes machine learning (ML) systems perfect for fighting fraud. When designed optimally, they learn, adapt, and uncover emerging patterns without the over-adaptation that can result in too many false positives.
Traditionally, organizations have relied on rules-based systems to detect fraud. Rules employ if-then logic that can be thorough at uncovering known patterns of fraud. And although rules remain an important fraud-fighting tool, especially in combination with advanced approaches, they are limited to recognizing patterns you already know and can program into the logic. They’re not effective at adapting to new fraud patterns, uncovering unknown schemes, or identifying increasingly sophisticated fraud techniques.