SAS: Fraud Detection and Machine Learning

Intro to a piece by SAS worth taking a look at.

Fraud detection and machine learning: What you need to know   from SAS

Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they represent a very small fraction of activity within an organization. The challenge is that a small percentage of activity can quickly turn into big dollar losses without the right tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters have learned to change their tactics. The good news is that with advances in fraud analytics, systems can learn, adapt and uncover emerging patterns for preventing fraud.

Most organizations still use rule-based systems as their primary tool to detect fraud. Rules can do an excellent job of uncovering known patterns; but rules alone aren’t very effective at uncovering unknown schemes, adapting to new fraud patterns, or handling fraudsters’ increasingly sophisticated techniques. This is where fraud analytics, powered by machine learning, becomes necessary for fraud prevention and detection.

Machine learning is all the rage now. Most vendors claim they have some form of machine learning, especially for fraud detection. SAS has been a pioneer in machine learning since the 1980s, when neural networks were first used to combat credit card fraud. But just because we’ve been doing machine learning and fraud analytics for so long doesn’t mean we’ve been resting on our laurels. In fact, it’s quite the opposite.

Machine learning is a critical part of the fraud detection toolkit. Here’s what you’ll need to get your fraud analytics initiative started. ... "