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

Aug 3, 2026

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Financial markets face growing complexity and rapidly expanding data volumes, making conventional risk monitoring methods insufficient for modern institutions. This paper designs and optimizes a big data and machine learning–driven risk monitoring system using a four-layer architecture to integrate large-scale financial data with advanced models. LSTM, Random Forest, and Gradient Boosting Trees are combined with real-time processing via Apache Flink to deliver timely, accurate risk alerts and decision support, with results showing improved efficiency and accuracy, especially for detecting and warning against market crash risks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Design of Big Data and Machine Learning-Based Risk Monitoring System

System Architecture

Data Collection and Preprocessing Module

Risk Identification and Assessment Module

Warning and Decision Support Module

Design and Optimization of Big Data and Machine LearningBased Risk Monitoring System in Financial Markets Liyang Wang1,a*, Yu Cheng2,b, Xingxin Gu3,c and Zhizhong Wu4,d 1Olin Business School, Washington University in St. Louis, Olin Business School, St. Louis, MO, 22102, USA 2The Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, 10027, USA 3College of Professional Studies, Northeastern University, Boston, MA, 02115, USA 4Independent Researcher, Mountain View, California, 94043, USA aEmail: [liyang.wang@163.com](liyang.wang@163.com) [b](bEmail: yucheng576@gmail.com)[Email: yucheng576@gmail.com](bEmail: yucheng576@gmail.com) [c](cEmail: gu.xingx@northeastern.edu)[Email: gu.xingx@northeastern.edu](cEmail: gu.xingx@northeastern.edu) dEmail: [ecthelion.w@gmail.com](ecthelion.w@gmail.com) *Corresponding Author 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 largescale 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. Keywords: Financial Risk Monitoring, Big Data, Risk Monitoring I. INTRODUCTION In financial markets, traditional risk monitoring methods are increasingly inadequate due to the complexity of transactions and the vast amount of data involved. With the rapid advancement of big data technology and machine learning algorithms, new tools have emerged to tackle these challenges. This paper explores the design and optimization of a risk monitoring system based on these advanced technologies. By integrating large-scale financial data and intelligent algorithms, this system not only monitors market risks in real time but also predicts potential risk points, thereby providing timely risk alerts and decision support for decision-makers. This significantly enhances the efficiency and accuracy of risk management. II. DESIGN OF BIG DATA AND MACHINE LEARNING-BASED RISK MONITORING SYSTEM A. System Architecture In the risk monitoring system, a four-layer hierarchical architecture is employed to efficiently process and analyze large-scale financial data. The data layer, at the bottom, is responsible for collecting raw data from various financial market sources, such as transaction records, market indicators, and news reports [1]. The computation layer utilizes various machine learning algorithms to process and analyze data, extracting valuable information. This includes using Long Short-Term Memory (LSTM) networks for handling time-series data to capture market dynamics. The application layer performs risk assessment and generates alerts based on outputs from the computation layer, utilizing model algorithms such as Random Forest and Gradient Boosting Trees to support decision-making. The topmost presentation layer visualizes the analysis results through a graphical user interface, enabling users to intuitively understand the risk situation. The architecture of the entire system aims to ensure efficient management and real-time processing of data flows, thereby achieving the capability to swiftly respond to market changes. Figure 1: Overall System Architecture B. Data Collection and Preprocessing Module In the risk monitoring system, the data collection and preprocessing module plays a crucial role. This module gathers financial mark

FAQs

What problem does the proposed system address in financial markets?

Traditional risk monitoring becomes inadequate due to transaction complexity and the vast volume of data. The system targets real-time monitoring and prediction under these conditions.

How does the system architecture work?

It uses a four-layer hierarchy: data collection, computation with machine learning, application for risk assessment and alerts, and a presentation layer for visualization through a GUI.

Which models and technologies are used for risk identification and real-time processing?

Risk identification leverages LSTM for financial time-series patterns, while Random Forest and Gradient Boosting Trees support decision-making. Apache Flink enables real-time data processing to maintain accuracy and timeliness.

Aug 3, 2026

Upload by Oliver