摘 要
财务舞弊行为严重损害了资本市场的健康发展，基于此背景，本文构建基于大数据的财务舞弊检测模型以期提高检测效率与准确性。随着信息技术发展，企业数据量呈爆炸式增长，传统检测方法难以应对海量复杂数据，本研究旨在利用大数据技术挖掘潜在舞弊特征。通过收集涵盖多种类型企业的海量财务与非财务数据，运用数据清洗、特征工程等预处理手段，选取逻辑回归、支持向量机、随机森林等机器学习算法建立模型。实验结果表明，该模型在准确率、召回率等指标上表现优异，相较于传统方法有显著提升。创新点在于融合多源异构大数据，突破单一财务数据局限，从更全面视角捕捉舞弊信号；同时引入非财务指标如管理层薪酬、公司治理结构等，丰富了特征维度。此外，模型构建过程中采用集成学习策略优化算法性能，为财务舞弊检测提供了新思路与有效工具，对维护资本市场秩序具有重要意义。
关键词：财务舞弊检测 大数据技术 机器学习算法
Abstract
Financial fraud significantly undermines the healthy development of capital markets. In this context, this paper constructs a financial fraud detection model based on big data to enhance detection efficiency and accuracy. With the advancement of information technology, corporate data volumes have experienced explosive growth, posing challenges that traditional detection methods struggle to address due to their inability to handle large and complex datasets. This study aims to leverage big data technology to uncover potential fraud characteristics. By collecting massive financial and non-financial data from various types of enterprises, we employ data preprocessing techniques such as data cleaning and feature engineering. Machine learning algorithms including logistic regression, support vector machines, and random forests are selected to build the model. Experimental results demonstrate superior performance in metrics such as accuracy and recall, showing significant improvements over traditional methods. The innovation lies in integrating multi-source heterogeneous big data, transcending the limitations of single-source financial data, and capturing fraud signals from a more comprehensive perspective. Additionally, non-financial indicators such as executive compensation and corporate governance structures are incorporated, enriching the feature dimensions. Furthermore, ensemble learning strategies are adopted during the model construction process to optimize algorithm performance, providing new insights and effective tools for financial fraud detection, which holds significant importance for maintaining the order of capital markets.
Keyword:Financial Fraud Detection Big Data Technology Machine Learning Algorithms
目 录
引言 1
1财务舞弊检测的背景与意义 1
1.1财务舞弊现状分析 1
1.2大数据技术的应用价值 2
1.3研究目标与意义 2
2大数据在财务舞弊检测中的应用基础 3
2.1数据来源与预处理 3
2.2关键特征提取方法 3
2.3模型构建理论依据 4
3财务舞弊检测模型的构建 4
3.1模型架构设计原则 5
3.2算法选择与优化 5
3.3模型训练与验证 6
4模型应用及效果评估 6
4.1实际案例分析 6
4.2检测效果评价指标 7
4.3应用前景展望 7
结论 8
参考文献 9
致谢 10
