Three common models for real-time machine learning fraud detection include anomaly detection, which is aimed at detecting data outliers that deviate from normal patterns, as well as supervised and unsupervised machine learning. The former involves an algorithm trained to recognize patterns and determine outcomes, while the latter relies on an algorithm that processes unlabeled data to detect hidden relations between data points.