This document discusses anomaly and fraud detection using machine learning. It outlines different applications of anomaly detection such as cybersecurity and fraud detection. It compares supervised versus unsupervised learning approaches for financial sector applications. Specific algorithms discussed for unsupervised anomaly detection include isolation forest, DBSCAN, HDBSCAN, local outlier factor, and Gaussian mixture models.