Anomaly detection for fraud prevention

Build an anomaly detection system for transaction fraud. Approach: 1. Use Isolation Forest for unsupervised outlier detection. 2. Engineer features (transaction amount, time of day, location distance). 3. Set contamination parameter based on historical fraud rate. 4. Generate anomaly scores and flag top 1% as suspicious. 5. Create alerting system with precision/recall monitoring. Visualize anomalies on a scatter plot with decision boundary. Balance false positives vs fraud detection.