A machine learning algorithm for fraud detection needs to be trained first by being fed the normal transaction data of lots and lots of cardholders. Transaction sequences are an example of this ...

Over time, as transaction volumes have exponentially grown, thousands of increasingly complicated fraud-detection rules emerged and this approach became intractable. Fortunately, machine learning can ...

New addition to Z series mainframe family uses IBM Telum processor to accelerate AI for real-time credit card fraud detection.

Bespoke fraud ML models are powered by algorithms that learn from historical data, picking up on behaviors and characteristics commonly associated with fraud.

Identity theft is common, but with the rise of AI and machine learning, its effect on the fintech industry has been reduced drastically. Here's how.

Whether it’s credit card fraud, identity theft, or phishing schemes, the ability to detect and respond to these threats in real time has become paramount.

Fighting Crime Using AI & Machine Learning Fraud Detection uses AI and machine learning algorithms to monitor monetary and non-monetary events and look for patterns that indicate possible risks.

Mastercard's AI-powered fraud detection system uses risk-scoring and behavioral biometrics to help identify suspicious transactions.

The end-of-year shopping whirlwind is underway. How does your credit card issuer watch out for fraudulent purchases on your account amid all those transactions?

Instead, the fraud detection efforts rely on machine learning, the subset of AI that excels at analyzing vast amounts of data, and making decisions and predictions based on what it’s learned.