The application of machine learning algorithms—including supervised, unsupervised, and deep learning models—to detect fraudulent patterns that rules-based systems cannot capture. ML models learn from historical fraud data to identify subtle, evolving patterns such as coordinated fraud rings, emerging attack vectors, and anomalous behavior. They continuously improve as new data becomes available, enabling adaptive and scalable fraud prevention.