Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning
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Abstract
Corporate tax planning has traditionally relied on manual processes and expert judgment to minimize tax liabilities. However, the rise of artificial intelligence (AI) presents new opportunities to transform these practices through automation, real-time analysis, and predictive capabilities. To conduct this research, a comprehensive review of academic literature, case studies, and existing AI tools in tax planning was carried out. The proposed AI-based tax planning model was developed based on these findings and validated through simulations and expert feedback. The study also examined the process of integrating AI systems with existing corporate tax structures to assess the feasibility and effectiveness of implementation. Results show that AI-driven corporate tax planning significantly enhances accuracy and efficiency compared to traditional methods. The AI model provides real-time analysis and predictive insights, enabling businesses to optimize tax strategies while ensuring compliance with evolving regulations. Tax authorities also benefit from AI implementation, gaining more transparent and equitable tax structures, along with stronger supervision capabilities. The proposed model can be developed into customized software tailored to individual companies, seamlessly integrating with existing corporate systems. These findings indicate that AI has the potential to revolutionize corporate tax planning by streamlining tax management and optimizing outcomes. For businesses, AI enables personalized tax strategies that adapt to specific data and regulations, improving both compliance and long-term tax optimization.
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