What is Unsupervised Machine Learning?
Unsupervised machine learning uncovers data patterns without labeled examples of desirable and undesirable outcomes. In fraud and compliance, this means clustering similar entities together, identifying weird outliers, and surfacing features that trip up supervised models. Techniques in the toolbox include clustering (k‑means, DBSCAN), anomaly detection (Isolation Forest, autoencoders), and embedding techniques that reduce the dimensionality of high‑dimensional telemetry.
Useful for: new products with few or no labels, early detection of mule rings, monitoring for drift when attackers change tactics. Risky for: false alarms if the data changes for benign reasons, like seasonality, promotions, new markets, or fraudsters gaming the system. Treat outputs as leads to follow up on, not verdicts.
