Automated Forensic Financial Anomaly Detection System

Design a comprehensive Python-based financial forensics system that uses advanced machine learning and statistical techniques to detect complex financial anomalies and potential fraud. Implement unsupervised and supervised anomaly detection algorithms, including isolation forests, local outlier factor, and deep learning autoencoders. Create a modular system that can process multiple financial data streams, generate risk scores, and provide detailed forensic reports integrated with Google Sheets.