Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets
This addresses risk monitoring challenges for financial institutions, but it is incremental as it applies existing methods like LSTM and Random Forest to financial data.
The paper tackled the problem of inadequate traditional risk monitoring in financial markets by designing and optimizing a big data and machine learning-based system, which significantly enhanced efficiency and accuracy in risk management, particularly in identifying market crash risks.
With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data and machine learning. By constructing a four-layer architecture, it effectively integrates large-scale financial data and advanced machine learning algorithms. Key technologies employed in the system include Long Short-Term Memory (LSTM) networks, Random Forest, Gradient Boosting Trees, and real-time data processing platform Apache Flink, ensuring the real-time and accurate nature of risk monitoring. Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management, particularly excelling in identifying and warning against market crash risks.