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

Paper: arXiv 2407.19352

Authors: 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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a standard high-level architecture combining off-the-shelf software (Hadoop, Flink) and machine learning models (LSTM, Random Forest) without introducing novel methodologies or deep theoretical formulations. The mathematical content is trivial (basic LSTM equations and Bayes’ theorem definition), and the empirical results rely on simple accuracy/AUC metrics without rigorous statistical testing or detailed trading backtests.

Research Flowchart

  flowchart TD
    A[Goal: Real-time Financial Risk Monitoring System] --> B[Input Data: Large-Scale Multi-Asset Financial Data]
    B --> C[4-Layer Architecture Implementation]
    C --> D[Real-Time Analytics: Apache Flink]
    C --> E[ML Modeling: LSTM, Random Forest, Gradient Boosting]
    D --> F[Integrated Big Data & Machine Learning Framework]
    E --> F
    F --> G[Outcome: High-Efficiency Risk Warnings & Market Crash Detection]