3 Key Ways Machine Learning Powers Fraud Prevention

In the digital age, fraud is an ever-lurking specter for businesses, financial institutions, and online merchants. Hackers and cybercriminals have become part of our shared consciousness. And they’re just as deeply embedded in our shared online ecosystem. Today, fraudsters employ increasingly sophisticated methods to perpetrate fraud, using new tools like AI. For the victims, this causes not only significant economic damage but undermines trust as well.

But the machines aren’t loyal to the fraudsters alone. Cutting-edge fraud platforms rely on advanced analytics and machine learning to combat fraud. To better understand how machine learning detects and prevents fraud, we first need to look at some of its key fraud detection techniques.

Blacklists, Whitelists, and Rules Engines

Blacklists are databases of known fraudulent entities, such as IP addresses, email IDs, or card numbers, while whitelists contain trusted entities. Transactions from blacklisted entities are denied, while those from whitelisted ones are allowed. However, while this method is simple and quick, it fails to catch new fraudsters not yet on the blacklist.

Rules-based systems work by establishing a set of rules that define fraudulent behavior. When a transaction breaches these predefined rules, the engine triggers an alert. Although rules engines can be highly effective when fraud patterns are known, they need an experienced analyst to write them and can struggle with novel or evolving fraud techniques.

Anomaly detection algorithms identify unusual behavior patterns that deviate significantly from expected patterns. This approach is particularly useful in detecting new types of fraud. However, defining what constitutes “normal” behavior can be a challenge and may lead to false positives.

Supervised Machine Learning (SML)

Supervised machine learning involves training an algorithm using labeled data (fraudulent and non-fraudulent transactions) to make predictions on unseen data. The “supervised” element of this type of machine learning refers to the supervision it requires, specifically data labeling. This technique is highly effective when a large, accurately labeled dataset is available. Likewise, as more data becomes available, the model’s accuracy improves over time.

However, obtaining such data can be challenging. Supervised machine learning models may also struggle to detect fraud patterns not present in their training data. These systems function best when the attacks they’re looking for are well-defined and don’t vary much over time. To cover the gaps that supervised machine learning leaves, we need a more powerful and adaptive model.

Unsupervised Machine Learning (UML)

Unsupervised machine learning is the yin to supervised machine learning’s yang. It does not require labeled data, making it proactive by nature. Unsupervised models identify patterns and anomalies in data that can signify fraudulent transactions. It can even discover new fraud patterns.

What’s more, unsupervised machine learning enables fraud platforms to leverage a complete approach that widens the scope of detection and increases accuracy. That’s why DataVisor provides customers full protection with our ensemble approach — our comprehensive fraud solution encompasses UML, SML, and rules engine.

Take the table below which compares each framework:

While both rules engines and supervised machine learning play key parts, unsupervised machine learning is the tool that unlocks a fraud platform’s full potential and truly protects against new and evolving fraud vectors.

Want to dive deeper into machine learning techniques that fight fraudsters, even in real time? DataVisor’s Sr. Director of Product Management Jeremy Chen recently held a webinar on this exact topic. To hear his deep dive into how these techniques work and the best ways to evaluate machine learning-based fraud detection services, download the webinar for free.