概括
机器学习模型,特别是随机森林模型,在评估制造业和服务业的税收风险方面表现出很高的准确性. 这为企业提供了更智能的审计和智能风险管理.
科学领域:
- 金融风险管理 金融风险管理
- 应用机器学习应用机器学习
- 税收和审计 税收和审计
背景情况:
- 企业面临复杂的财务数据和相互关联的风险.
- 机器学习为增强税收风险评估和审计提供了机会.
研究的目的:
- 评估三种机器学习模型 (SVM,XGBoost,Random Forest) 在税务风险评估中的有效性.
- 确定制造业和服务业的关键风险指标.
主要方法:
- 分析了来自区域制造业和服务业的3232份税务记录 (2021-2023年).
- 支持矢量机 (SVM),XGBoost和随机森林预测模型的比较评估.
主要成果:
- 随机森林实现了卓越的准确性:92.00% (制造) 和93.39% (服务).
- 关键制造业风险指标:税收负担率,利波动,审计频率.
- 关键服务业风险指标:利波动.
结论:
- 机器学习,特别是随机森林,对于税务风险分析是有效的.
- 调查结果为监管机构提供了风险管理和智能审计的智能工具.
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