Collaborative Approaches for Global Tax Compliance in Digital Platforms: Challenges and Solutions
Main Article Content
Abstract
The rise of digital platforms has revolutionized global commerce, leading to new challenges in tax compliance. As businesses expand across borders, the complexity of managing tax obligations across multiple jurisdictions becomes increasingly difficult. The traditional tax systems were designed for physical goods and services, and their adaptation to the digital economy has been slow. This has led to inconsistent tax frameworks, creating opportunities for tax evasion and avoidance. Collaborative approaches, involving governments, international organizations, and private sector stakeholders, are essential for addressing these challenges.
This paper explores the key challenges faced by global tax systems in the context of digital platforms, including the lack of standardized regulations, data privacy concerns, and the difficulty of tracking cross-border transactions. It further discusses solutions to these issues, such as the implementation of digital tax frameworks, the use of advanced technologies like blockchain for real-time data tracking, and the development of international tax treaties tailored to the digital economy. Additionally, the role of cooperation among tax authorities and the importance of transparency in tax reporting are examined. By fostering collaboration, both at the domestic and international levels, tax compliance in the digital space can be significantly improved, ensuring fairness and accountability. The paper concludes by emphasizing the need for ongoing dialogue and innovation to address the evolving landscape of global tax compliance in digital platforms.
This abstract provides a comprehensive overview of the current state of global tax compliance in digital platforms and highlights potential solutions through collaborative efforts.
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References
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• Shashank Shekhar Katyayan, Dr. Saurabh Solanki, Leveraging Machine Learning for Dynamic Pricing Optimization in Retail , IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.11, Issue 4, Page No pp.29-50, December 2024, Available at : http://www.ijrar.org/IJRAR24D3371.pdf
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• Piyush Bipinkumar Desai, Dr. Lalit Kumar,, Data Security Best Practices in Cloud-Based Business Intelligence Systems , IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.11, Issue 4, Page No pp.158-181, December 2024, Available at : http://www.ijrar.org/IJRAR24D3378.pdf
• Changalreddy, V. R. K., & Vashishtha, S. (2024). Predictive analytics for reducing customer churn in financial services. International Journal for Research in Management and Pharmacy (IJRMP), 13(12), 22. https://www.ijrmp.org
• Gudavalli, S., Bhimanapati, V., Mehra, A., Goel, O., Jain, P. A., & Kumar, D. L. (2024). Machine Learning Applications in Telecommunications. Journal of Quantum Science and Technology (JQST), 1(4), Nov(190–216). https://jqst.org/index.php/j/article/view/105
• Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.
• Singh, S. P. & Goel, P. (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.
• Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh
• Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.
• Kammireddy, V. R. C., & Goel, S. (2024). Advanced NLP techniques for name and address normalization in identity resolution. International Journal of Research in Modern Engineering and Emerging Technology, 12(12), 600. https://www.ijrmeet.org
• Vinay kumar Gali, Prof. (Dr) Punit Goel, Optimizing Invoice to Cash I2C in Oracle Cloud Techniques for Enhancing Operational Efficiency , IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.11, Issue 4, Page No pp.51-70, December 2024, Available at : http://www.ijrar.org/IJRAR24D3372.pdf
• Natarajan, Vignesh, and Prof. (Dr) Punit Goel. 2024. Scalable Fault-Tolerant Systems in Cloud Storage: Case Study of Amazon S3 and Dynamo DB. International Journal of All Research Education and Scientific Methods 12(12):4819. ISSN: 2455-6211. Available online at www.ijaresm.com. Arizona State University, 1151 S Forest Ave, Tempe, AZ, United States. Maharaja Agrasen Himalayan Garhwal University, Uttarakhand. ORCID.
• Kumar, A., & Goel, P. (Dr) P. (2025). Enhancing ROI through AI-Powered Customer Interaction Models. Journal of Quantum Science and Technology (JQST), 2(1), Jan(585–612). Retrieved from https://jqst.org/index.php/j/article/view/178
• Bajaj, A., & Prasad, P. (Dr) M. (2025). Data Lineage Extraction Techniques for SQL-Based Systems. Journal of Quantum Science and Technology (JQST), 2(1), Jan(388–415). Retrieved from https://jqst.org/index.php/j/article/view/170
• Pingulkar, Chinmay, and Shubham Jain. 2025. Using PFMEA to Enhance Safety and Reliability in Solar Power Systems. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 13(1):1–X. Retrieved (https://www.ijrmeet.org).
• Venkatesan, Karthik, and Saurabh Solanki. 2024. Real-Time Advertising Data Unification Using Spark and S3: Lessons from a 50GB+ Dataset Transformation. International Journal of Research in Humanities & Social Sciences 12(12):1-24. Resagate Global - Academy for International Journals of Multidisciplinary Research. Retrieved (www.ijrhs.net).
• Sivaraj, K. P., & Singh, N. (2025). Impact of Data Visualization in Enhancing Stakeholder Engagement and Insights. Journal of Quantum Science and Technology (JQST), 2(1), Jan(519–542). Retrieved from https://jqst.org/index.php/j/article/view/175
• Rao, Priya Guruprakash, and Abhinav Raghav. 2025. Enhancing Digital Platforms with Data-Driven User Research Techniques. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 13(1):84. Resagate Global - Academy for International Journals of Multidisciplinary Research. Retrieved (https://www.ijrmeet.org).
• Mulka, Arun, and Dr. S. P. Singh. 2025. “Automating Database Management with Liquibase and Flyway Tools.” International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 13(1):108. Retrieved (www.ijrmeet.org).
• Mulka, A., & Kumar, D. R. (2025). Advanced Configuration Management using Terraform and AWS Cloud Formation. Journal of Quantum Science and Technology (JQST), 2(1), Jan(565–584). Retrieved from https://jqst.org/index.php/j/article/view/177
• Gupta, Ojas, and Lalit Kumar. 2025. “Behavioral Economics in UI/UX: Reducing Cognitive Load for Sustainable Consumer Choices.” International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 13(1):128. Retrieved (www.ijrmeet.org).
Somavarapu, S., & ER. PRIYANSHI. (2025). Building Scalable Data Science Pipelines for Large-Scale Employee Data Analysis. Journal of Quantum Science and Technology (JQST), 2(1), Jan(446–470). Retrieved from https://jqst.org/index.php/j/article/view/172
• Workload-Adaptive Sharding Algorithms for Global Key-Value Stores , IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 8, page no.e594-e611, August-2023, Available :https://ijnrd.org/papers/IJNRD2308458.pdf
• ML-Driven Request Routing and Traffic Shaping for Geographically Distributed Services , IJCSPUB - INTERNATIONAL JOURNAL OF CURRENT SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.10, Issue 1, page no.70-91, February-2020, Available :https://rjpn.org/IJCSPUB/papers/IJCSP20A1010.pdf
• Automated Incremental Graph-Based Upgrades and Patching for Hyperscale Infrastructure , IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.6, Issue 6, page no.89-109, June-2021, Available :https://ijnrd.org/papers/IJNRD2106010.pdf
• Chintha, Venkata Ramanaiah, and Punit Goel. 2025. “Federated Learning for Privacy-Preserving AI in 6G Networks.” International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 13(1):39. Retrieved (http://www.ijrmeet.org).
• Chintha, V. R., & Jain, S. (2025). AI-Powered Predictive Maintenance in 6G RAN: Enhancing Reliability. Journal of Quantum Science and Technology (JQST), 2(1), Jan(495–518). Retrieved from https://jqst.org/index.php/j/article/view/173