Sustainable Economic Growth Through Artificial Intelligence -Driven Tax Frameworks Nexus on Enhancing Business Efficiency and Prosperity; An Appraisal

Abstract: The article examines the nexus between Artificial Intelligence (AI)-driven tax frameworks and sustainable economic growth, with a focus on enhancing business efficiency and prosperity. The research made use of explorative method. As governments and businesses face challenges like climate change, resource depletion, and income inequality, AI offers transformative potential in optimizing tax frameworks. By leveraging AI technologies such as machine learning, data analytics, and natural language processing, tax systems can become more efficient, equitable, and transparent. The paper proposed optimized tax collection strategies driven by artificial intelligence. The paper recommended the need to address technical, regulatory, and operational challenges by focusing on strategies targeting each of them can tax authorities and businesses employ the full potential of AI-driven tax frameworks.

Artificial Intelligence (AI), Machine Learning, Economic Growth, Tax Optimization

1. Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity, and poverty. Crown Business. [Google Scholar] [Crossref]

2. Acemoglu, D., & Restrepo, P. (2020). "Artificial Intelligence, Automation, and Work." Journal of Economic Perspectives, 34(4), 3-30. [Google Scholar] [Crossref]

3. Alonso, A., & Li, J. (2021). "AI in Tax Administration: Challenges and Opportunities." Journal of Tax Administration, 7(2), 45-61. [Google Scholar] [Crossref]

4. Anwansedo, F., Gbadebo, A. D., & Akinwande, O. T. (2024). Exploring the Role of AI-Enhanced Online Marketplaces in Facilitating Economic Growth: An Impact Analysis on Trade Relations between the United States and Sub-Saharan Africa. Revista De Gestão Social E Ambiental, 18(6), e07494. https://doi.org/10.24857/rgsa.v18n6-139 [Google Scholar] [Crossref]

5. Bloom, N., Schankerman, M., & Van Reenen, J. (2019). "Identifying technology spillovers and product market rivalry." Econometrica, 87(5), 1341-1371. [Google Scholar] [Crossref]

6. Brynjolfsson, E., & McAfee, A. (2017). "The Business of Artificial Intelligence: What it Can and Cannot – Do for Your Organization." Harvard Business Review. [Google Scholar] [Crossref]

7. Chen, H., Chiang, R. H. L., & Storey, V. C. (2019). "Business Intelligence and Analytics: From Big Data to Big Impact." MIS Quarterly, 36(4), 1165-1188 [Google Scholar] [Crossref]

8. Cockfield, A. J. (2020). "Big Data and Tax Haven Secrecy." Canadian Business Law Journal, 61(2), 206-226. [Google Scholar] [Crossref]

9. Eichhorst, W., & Marx, P. (2021). "AI and the Future of Work: How Artificial Intelligence is Shaping Labor Markets." IZA Journal of Labor Policy, 10(1), 12-25 [Google Scholar] [Crossref]

10. Eichner, T., & Pethig, R. (2019). "Renewable energy subsidies: Second-best policy or fatal aberration for efficient mitigation?" Journal of Public Economics, 170, 67-79. [Google Scholar] [Crossref]

11. Elliott, L. (2019). "Green Growth: Ideology, Political Economy and the Alternatives." International Journal of Political Economy, 48(1), 1-14. [Google Scholar] [Crossref]

12. Hoffman, L. F., et al. (2020). "AI and Taxation: The Potential for Transformative Change." International Journal of Economic Policy in Emerging Economies, 13(3), 267-284. [Google Scholar] [Crossref]

13. Kokina, J., & Davenport, T. H. (2017). "The Emergence of Artificial Intelligence: How Automation is Changing Tax Preparation." Journal of Emerging Technologies in Accounting, 14(1), 115-122. [Google Scholar] [Crossref]

14. Oladele, I., Orelaja, A., & Akinwande, O., (2024). Ethical Implications and Governance of Artificial Intelligence in Business Decisions: A Deep Dive into the Ethical Challenges and Governance Issues Surrounding the Use of Artificial Intelligence in Making Critical Business Decisions. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume XIII, Issue II, DOI: https://doi.org/10.51583/IJLTEMAS.2024.130207 [Google Scholar] [Crossref]

15. Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press. [Google Scholar] [Crossref]

16. Sachs, J. D. (2015). "The Age of Sustainable Development." Columbia University Press. [Google Scholar] [Crossref]

17. Slemrod, J., & Gillitzer, C. (2013). Tax systems. MIT Press. [Google Scholar] [Crossref]

18. Stiglitz, J. E. (2018). "The Welfare State in the Twenty-First Century." Journal of Public Economics, 162, 4-17. [Google Scholar] [Crossref]

19. Tadesse, B. W., & Shiferaw, M. (2021). "Machine Learning for Tax Evasion Detection: An Overview." International Journal of Public Administration in the Digital Age, 8(1), 1-12. [Google Scholar] [Crossref]

20. Veale, M., & Brass, I. (2019). "Administration by Algorithm? Public Management Meets Public Sector Machine Learning." International Journal of Public Administration, 42(13), 1151 1167. [Google Scholar] [Crossref]

• Exploration of Organizational Culture on Employee Well-Being: A Survey-Based Study on Workplace Culture Dimensions
• Peecheck 2.0: Design and Improvement of Rapid and Low-Cost Urine Analysis Device for Rural Health Care
• Energy Mixed More Fuels with Lower Carbon Contain and Renewable Energy Reduce Carbon Dioxide Emissions: A Review
• Federalism and National Integration in Nigeria: A Study of Selected States in South-South, Nigeria
• The Seasonal Variations in Hydrological Factors in the Activity Zones: An Essential Examination of the Ecosystem in Badagry Creek, Lagos, Nigeria