Unsupervised anomaly detection on 284K credit card transactions (0.17% fraud). Models train only on normal data and learn to flag deviations. Compares Isolation Forest, Local Outlier Factor, and One-Class SVM, revealing the precision-recall tradeoff in extreme class imbalance.