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

Author

Listed:
  • Shallon Asiimire

    (Courage Obofoni Esechie, Southern University, Kalma US.)

  • Baton Rouge.

    (Courage Obofoni Esechie, Southern University, Kalma US.)

  • Fechi George Odocha

    (Courage Obofoni Esechie, Southern University, Kalma US.)

  • Friday Anwansedo

    (Courage Obofoni Esechie, Southern University, Kalma US.)

  • Oluwaseun Rafiu Adesanya

    (Courage Obofoni Esechie, Southern University, Kalma US.)

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.

Suggested Citation

  • Shallon Asiimire & Baton Rouge. & Fechi George Odocha & Friday Anwansedo & Oluwaseun Rafiu Adesanya, 2024. "Sustainable Economic Growth Through Artificial Intelligence -Driven Tax Frameworks Nexus on Enhancing Business Efficiency and Prosperity; An Appraisal," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 13(9), pages 44-52, September.
    • Handle: RePEc:bjb:journl:v:13:y:2024:i:9:p:44-52
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