Machine Learning-Driven Financial Anomaly Detection Database

Architect a machine learning-enabled database system for detecting financial anomalies using Laravel and advanced statistical modeling. Design a solution that can ingest massive financial transaction datasets, implement real-time anomaly detection algorithms, and provide scalable storage for machine learning models. Develop a comprehensive approach that supports model training, inference, and maintains high-performance transaction analysis capabilities.