基于机器学习的企业财务审计框架与高风险识别
Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
AI总结：
本研究提出基于机器学习的企业财务审计框架，利用随机森林算法实现高风险识别，提升审计效率和准确性。
摘要：
在全球经济不确定性面前，财务审计已成为监管合规和风险缓解的关键环节。传统的人工审计方法日益受到大数据量、复杂业务结构和演变欺诈手段的限制。本研究提出了一种基于人工智能的企业财务审计和高风险识别框架，利用机器学习提高效率和准确性。使用来自四大会计事务所（埃森哲、普华永道、德勤、毕马威）2020至2025年的数据集，研究分析了风险评估趋势、合规违规和欺诈检测。数据集包括关键指标，如审计项目数量、高风险案例、欺诈实例、合规违规、员工工作量和客户满意度，涵盖了审计行为和人工智能对运营的影响。为了构建稳健的风险预测模型，评估了三种算法——支持向量机（SVM）、随机森林（RF）和K近邻（KNN）。SVM使用超平面优化进行复杂分类，RF结合决策树来管理高维、非线性数据并具有抗过拟合能力，KNN应用基于距离的学习方法实现灵活性能。通过层次K折交叉验证和使用F1分数、准确率和召回率进行评估，随机森林取得了最佳性能，F1分数为0.9012，在识别欺诈和合规异常方面表现突出。特征重要性分析揭示了审计频率、以往违规、员工工作量和客户评分为主要预测因子。研究建议采用随机森林作为核心模型，通过特征工程增强特征，并实施实时风险监控。本研究为现代企业利用机器学习进行智能审计和风险管理提供了有价值的见解。
英文摘要：
In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI-driven framework for enterprise financial audits and high-risk identification, leveraging machine learning to improve efficiency and accuracy. Using a dataset from the Big Four accounting firms (EY, PwC, Deloitte, KPMG) from 2020 to 2025, the research examines trends in risk assessment, compliance violations, and fraud detection. The dataset includes key indicators such as audit project counts, high-risk cases, fraud instances, compliance breaches, employee workload, and client satisfaction, capturing both audit behaviors and AI's impact on operations. To build a robust risk prediction model, three algorithms - Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) - are evaluated. SVM uses hyperplane optimization for complex classification, RF combines decision trees to manage high-dimensional, nonlinear data with resistance to overfitting, and KNN applies distance-based learning for flexible performance. Through hierarchical K-fold cross-validation and evaluation using F1-score, accuracy, and recall, Random Forest achieves the best performance, with an F1-score of 0.9012, excelling in identifying fraud and compliance anomalies. Feature importance analysis reveals audit frequency, past violations, employee workload, and client ratings as key predictors. The study recommends adopting Random Forest as a core model, enhancing features via engineering, and implementing real-time risk monitoring. This research contributes valuable insights into using machine learning for intelligent auditing and risk management in modern enterprises.
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