Real-Time Anomaly Detection in Financial Streaming Data

Design a high-performance Python system for real-time financial transaction anomaly detection using streaming data processing. Implement a multi-stage approach combining Kafka for data ingestion, Apache Spark for distributed processing, and advanced machine learning techniques including isolation forests, local outlier factor, and deep autoencoder models. Create a modular architecture that supports dynamic model retraining, handles high-volume data streams, and provides real-time alerting for potential fraudulent activities.