Random forest model in tax risk identification of real estate enterprise income tax
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View abstract on PubMed
Summary
This summary is machine-generated.An optimized random forest model enhances tax risk identification in real estate. The model accurately assesses enterprise tax compliance risks and pinpoints specific risk areas, improving corporate income tax reporting.
Area Of Science
- Data Science
- Taxation
- Real Estate
Background
- Tax losses and risks in the real estate sector pose significant challenges.
- Existing tax risk identification methods may lack precision.
- The random forest model offers potential for improved tax risk assessment.
Purpose Of The Study
- To enhance the random forest model for identifying tax risks in real estate.
- To develop and validate a risk identification model using real taxpayer data.
- To improve the accuracy of corporate income tax reporting for real estate enterprises.
Main Methods
- Optimization of the random forest model for tax risk distinctiveness.
- Selection and analysis of key indicators for tax risk assessment.
- Empirical testing of the risk identification model with actual taxpayer data.
Main Results
- The model accurately identifies tax compliance risks, including value-added tax refund situations and suspicious items.
- Enterprise risk assessment revealed key areas such as operating income, selling expenses, and total profit.
- Significant discrepancies between model-judged and declared values indicate potential underreporting of corporate income tax.
Conclusions
- The optimized random forest model effectively enhances tax risk identification in the real estate industry.
- The model provides an intuitive understanding of tax-related risks and specific risk points.
- Accurate assessment of tax compliance risks can mitigate tax losses and improve reporting accuracy.
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