Year: 2024
Type: article
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.
Institution: East Texas A&M University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4404027649