Supervised learning: We provide the machine learning system with already labelled data, which is data that has been previously prepared and labeled as “nominal” or “anomaly”. Unsupervised learning: ...

Unsupervised anomaly detection uses an unlabeled test set of data. It involves training a machine learning (ML) model to identify normal behavior using an unlabeled dataset.

As fraud became more complex, supervised learning alone wasn’t sufficient. This led to the adoption of unsupervised learning, particularly for anomaly detection.