Design and Optimization of Financial Market Risk Monitoring System Based on Big Data Machine Learning

This article focuses on introducing large-scale data analysis techniques based on machine learning. By increasing the volume of financial data, traditional banking risk control tools no longer meet the development needs of modern financial markets. Therefore, we designed a risk monitoring system with a four-level architecture, including collection, processing, assessment and decision support. Specially designed for real-time analysis, using various complex mechanical algorithms such as random shafts and gradient shafts to detect major market risks. The recall rate of this system is 92%, which is better than other models. This system not only has excellent data processing capabilities and stability, but also has exceptional scalability, which can effectively analyze large data sets in real time. This project research will promote the integration of large data crossings and machine learning, improve the use of financial risk monitoring systems and achieve proper and effective risk management in complex market environments.