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Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets

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  • Liyang Wang
  • Yu Cheng
  • Xingxin Gu
  • Zhizhong Wu

Abstract

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.

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  • Liyang Wang & Yu Cheng & Xingxin Gu & Zhizhong Wu, 2024. "Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets," Papers 2407.19352, arXiv.org.
    • Handle: RePEc:arx:papers:2407.19352
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    File URL: http://arxiv.org/pdf/2407.19352
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    References listed on IDEAS

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    1. Zhennan Wu, 2022. "Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1607-1625, April.
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