Practices and Research on Enterprise Tax Risk Control in the Big Data Era
DOI:
https://doi.org/10.62051/ijgem.v7n2.12Keywords:
Big data, Corporate tax risk, Risk control, Machine learning, Tax complianceAbstract
In the context of rapid development in big data technology, enterprises face challenges such as surging data volumes and increased policy complexity in tax management. Traditional risk control methods struggle to meet real-time monitoring needs, making tax risk prevention a core issue in corporate financial management. By employing case analysis, questionnaire surveys, and data modeling techniques, combined with corporate tax data, a risk warning model based on machine learning is constructed. Big data technology significantly enhances the accuracy of tax risk identification, with invoice anomalies and imbalanced cost-expense matching being frequent risk points; the established risk control system can reduce the tax compliance costs for sample enterprises. Theoretically, this enriches cross-disciplinary research between big data and tax risk management, while practically providing decision-making references for optimizing corporate tax internal controls and precise regulatory oversight by tax authorities.
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