Real-Time Financial Anomaly Detection System

Design a Python-based financial anomaly detection system using advanced statistical and machine learning techniques. Implement a multi-layered approach that combines Z-score, Isolation Forest, and deep learning autoencoder methods to identify potential fraudulent transactions in streaming financial data. The system should provide real-time scoring, maintain a low false-positive rate (<2%), and generate interpretable risk scores with feature-level explanations.