Conceptual framework for AI-driven tax compliance in fintech ecosystems

Oritsematosan Faith Dudu 1, *, Olakunle Babatunde Alao 2 and Enoch O. Alonge 3

1 Independent Researcher, NJ, USA.
2 Independent Researcher, Seattle, WA, USA.
3 College of Business, Texas A&M University-Commerce, USA.
 
Review
International Journal of Frontiers in Engineering and Technology Research, 2024, 07(02), 001–010.
Article DOI: 10.53294/ijfetr.2024.7.2.0045
Publication history: 
Received on 16 September 2024; revised on 25 October 2024; accepted on 28 October 2024
 
Abstract: 
This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy issues, and regulatory hurdles. Strategies for overcoming these challenges and fostering broader adoption are discussed. Finally, the paper offers recommendations for fintech companies and policymakers, emphasizing the need for transparent AI models, bias mitigation, data privacy, and updated regulatory frameworks to ensure fair and effective AI-enabled tax systems.
 
Keywords: 
Artificial Intelligence (AI); Tax Compliance; Fintech Ecosystems; Machine Learning; Regulatory Challenges; Predictive Analytics​
 
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