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.
That’s why more and more industries are embracing ML, and artificial intelligence, for fraud detection. Recent research by SAS and the Association of Certified Fraud Examiners found that a mere 13% of organizations across industries take advantage of these technologies to detect and deter fraud. Another 25% plan to incorporate them into their anti-fraud programs over the next two years–a near 200% jump.
So, how does it work? Simply put, ML automates the extraction of known and unknown patterns from data. Once it recognizes those patterns, it can apply what it knows to new and unseen data. The machine learns and adapts as new outcomes and new patterns are presented to it via a feedback loop.
In fraud detection, supervised ML models attempt to learn from identified records in data, often referred to as labeled data. To train a supervised model, you present it both fraudulent and nonfraudulent records that have been labeled as such.
Unsupervised ML is different. When you don’t know what data is fraudulent, you ask the model to learn the data structure on its own. You simply present it with data, and the model attempts to understand the underlying structure and dimensions of that data.
To apply ML to fraud detection, at a minimum, you’ll need the following components:
Identifying nefarious transactions while delivering quality customer service is a delicate balancing act. An organization that frequently declines legitimate transactions or makes its authentication measures too cumbersome is apt to lose customers. ML systems are ideal for minimizing this type of friction.
For example, one global financial institution recently worked with SAS to modernize its rule-based fraud detection system and help strike a balance between oversight and customer service. To do this, the bank implemented an ML-based solution from SAS that uses an ensemble of neural networks to create two different fraud scores:
Using this dual-score approach, the financial institution correctly identified nearly $1 million in monthly transactions that had been erroneously identified as fraud. It was also able to find an additional $1.5 million per month in fraud that had previously gone undetected.
Fraud detection is a challenging problem. While fraudulent transactions represent a very small fraction of activity within an organization, a small percentage of activity can quickly turn into big dollar losses without the right tools and systems in place. With the advances in ML, systems can learn, adapt, and uncover emerging patterns for preventing fraud--so you can keep up with the fraudsters even as they evolve and change tactics.