摘 要
随着大数据技术的快速发展及其在各领域的广泛应用，会计风险监控正面临前所未有的机遇与挑战。传统会计风险监控方法受限于数据规模、维度和实时性，难以满足现代企业复杂多变的风险管理需求。本研究以大数据驱动为核心理念，旨在构建一种动态、智能且高效的会计风险监控框架，以提升风险识别的精准度和响应速度。研究采用数据挖掘、机器学习及可视化分析等多维技术手段，结合会计领域专业知识，设计了一套涵盖数据采集、清洗、建模与预测的全流程解决方案。通过对海量结构化与非结构化会计数据的深度分析，模型能够实时捕捉潜在风险信号并进行预警。实证研究表明，该方法较传统监控手段显著提高了风险识别的准确率和时效性，特别是在异常交易检测和财务欺诈预警方面表现突出。本研究的主要创新点在于将大数据技术与会计风险管理深度融合，提出了适应复杂环境的动态监控机制，并为相关理论研究和实践应用提供了新思路。研究成果不仅有助于企业优化内部控制体系，也为监管机构制定政策提供了科学依据，具有重要的学术价值和实际意义。
关键词：大数据驱动；会计风险监控；机器学习；动态监控机制；财务欺诈预警
Research on Dynamic Monitoring of Accounting Risks Driven by Big Data
Abstract: With the rapid development of big data technologies and their extensive application across various fields, accounting risk monitoring is facing unprecedented opportunities and challenges. Conventional methods for accounting risk monitoring are constrained by data scale, dimensionality, and real-time capabilities, making it difficult to meet the complex and dynamic risk management requirements of modern enterprises. This study, grounded in the core concept of big-data-driven innovation, aims to construct a dynamic, intelligent, and efficient fr amework for accounting risk monitoring to enhance the accuracy and responsiveness of risk identification. By employing multidimensional technical approaches such as data mining, machine learning, and visualization analysis, combined with domain-specific accounting knowledge, a comprehensive solution covering data collection, cleaning, modeling, and prediction has been designed. Through in-depth analysis of massive structured and unstructured accounting data, the model is capable of capturing latent risk signals in real time and issuing early warnings. Empirical studies demonstrate that this approach significantly improves the accuracy and timeliness of risk identification compared to traditional monitoring methods, particularly excelling in anomaly transaction detection and financial fraud alerts. The primary innovation of this research lies in the deep integration of big data technologies with accounting risk management, proposing a dynamic monitoring mechanism adaptable to complex environments while offering new perspectives for both theoretical exploration and practical applications. The findings not only contribute to optimizing internal control systems within enterprises but also provide scientific evidence for regulatory authorities in policy formulation, thereby holding significant academic value and practical implications.
Keywords: Big Data Driven; Accounting Risk Monitoring; Machine Learning; Dynamic Monitoring Mechanism; Financial Fraud Early Warning
目 录
一、绪论 1
（一）研究背景与意义 1
（二）国内外研究现状分析 1
（三）研究方法与技术路线 2
二、大数据驱动的会计风险识别 2
（一）会计风险的特征与分类 2
（二）大数据在风险识别中的应用 3
（三）风险识别的关键技术与算法 3
三、动态监控体系的设计与实现 4
（一）动态监控的核心要素 4
（二）数据采集与处理机制 4
（三）监控模型的构建与优化 5
四、实证分析与案例研究 5
（一）实证研究设计与数据来源 5
（二）案例分析：企业会计风险监控实践 6
（三）结果分析与改进建议 6
结论 7
参考文献 8
致 谢 9
