Predictive modeling of tax compliance risks: A comparative study of machine learning approaches
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models, particularly Random Forest, show high accuracy in assessing tax risks for manufacturing and service sectors. This enables smarter auditing and intelligent risk management for businesses.
Area Of Science
- Financial Risk Management
- Applied Machine Learning
- Taxation and Auditing
Background
- Enterprises face complex financial data and interconnected risks.
- Machine learning presents opportunities for enhanced tax risk assessment and auditing.
Purpose Of The Study
- To evaluate the efficacy of three machine learning models (SVM, XGBoost, Random Forest) for tax risk assessment.
- To identify key risk indicators in manufacturing and service sectors.
Main Methods
- Analysis of 3,232 tax records from regional manufacturing and service sectors (2021-2023).
- Comparative evaluation of Support Vector Machine (SVM), XGBoost, and Random Forest predictive models.
Main Results
- Random Forest achieved superior accuracy: 92.00% (manufacturing) and 93.39% (service).
- Key manufacturing risk indicators: tax burden rate, profit fluctuation, audit frequency.
- Key service sector risk indicator: profit volatility.
Conclusions
- Machine learning, especially Random Forest, is effective for tax risk analysis.
- Findings provide regulators with intelligent tools for risk management and smart auditing.
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