Data-Driven Tax Risk Management: Legal Strategies in the Era of Big Data

This paper proposes a tax risk prediction algorithm based on dynamic weights and smart contracts. It innovatively integrates multi-source tax data through a data preprocessing module, uses a dynamic weight calculation mechanism to achieve adaptive adjustment of risk indicators, and converts tax laws and regulations into smart contract rules and embeds them into a prediction model. The experiment selected declaration, invoice, and financial data of 2,000 enterprises in a certain region for the past three years, and conducted simulation verification in a Python environment. The results show that the algorithm has an accuracy rate of 92.3% in tax risk prediction, which is 15.7% higher than the traditional support vector machine algorithm and 12.4% higher than the random forest algorithm; the average processing time is 0.87 seconds, and the timeliness is more than 30% higher than the traditional algorithm. At the same time, under the constraints of smart contract legal rules, the accuracy rate of risk identification that meets the boundaries of legal disputes is 89.6%. This paper proposes a legal strategy optimization plan from three aspects: improving data collection and use specifications, building an algorithm supervision framework, and strengthening risk response guarantees, providing a new path for the integration of technology and law for tax risk management in the era of big data.