Why should your business get on board with machine learning? Because it's changing the landscape of fraud and authentication. Read the Aite report on how machine learning is becoming a competitive issue.

Hear from Michael Thelander, fraud and authentication expert and author of Multi-factor Authentication for Dummies, about the definition of machine learning in the context of fraud prevention.

“What is machine learning?

One of the ways to describe what machine learning is, is to describe what it’s not. Most of the systems that we make for fraud prevention and authentication, for information security, are based on rules. Human rules and business rules that we apply manually. We know what the situation is that we’re looking for; we make rules to catch that situation and flag it.

Machine learning takes a completely different approach. It looks at all of the activity in those transactions, or all of the activity in those particular user channels, and tries to determine what is normal. What can be expected as far as a normal activity? And then using that, highlight what’s not normal.

With machine learning, then, you don’t have to know everything in advance. You get this much broader pallet of ‘here’s all of the activity’, and we can now apply different algorithms to that activity that tell us, ‘this looks like risky activity’, or ‘this looks like trustworthy activity’ and determine by the organization, or even by vertical segment, where does that curve actually fit? And where do the transactions, our customers, fit within that curve?”

For more information, check out our blog. Or download our Definitive Guide™ to Next-Generation Fraud Prevention eBook.