Transforming Tax Compliance with Machine Learning: Reducing Fraud and Enhancing Revenue Collection
Samuel Oladiipo Olabanji, Oluwaseun Oladeji Olaniyi, Olugbenga Olaposi Olaoye
Asian Journal of Economics, Business and Accounting · pp. 503–513 · Published 18 Nov 2024
10.9734/ajeba/2024/v24i111572Abstract
The integration of machine learning (ML) in tax administration has the potential to revolutionize tax compliance, enhancing fraud detection and optimizing revenue collection. This literature review explores the application of ML in tax systems, emphasizing its transformative role in addressing the limitations of traditional, labor-intensive compliance methods. Justification for adopting a literature review approach is rooted in the need to consolidate diverse perspectives, address research gaps, and provide an informed synthesis of existing findings. The study highlights the criteria used for selecting case studies and research papers, ensuring a robust analysis of ML’s ability to automate detection processes, improve risk assessment, and enable predictive analytics for efficient tax administration. Despite its potential, ML adoption is challenged by data quality issues, privacy concerns, technical infrastructure demands, and ethical considerations, which must be systematically addressed. This paper also identifies literature gaps, particularly the lack of balanced discourse, and provides recommendations for overcoming barriers, including enhancing data management practices, adopting ethical frameworks, and fostering cross-border collaboration. By addressing these challenges, ML can equip tax authorities with tools for creating efficient, adaptive, and fair systems. This research underscores ML’s growing importance in transforming global tax compliance, setting the stage for a future of more responsive and effective revenue administration.
Cited by 23
R. K. Mishra, R. C. Poonia, Abhishek Maheshwari · 2026 International Conference on NextGen Data Science and Analytics (ICNDSA) · 2026
Ugarthi Shankalia M, Petikam Sailaja, S. Selvaraju · 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) · 2026
Dushime Yvonne · International Journal of Innovative Science and Research Technology · 2025
Angeline Shane, Helena Jelivia Tan Wijaya, Gatot Soepriyanto · Edelweiss Applied Science and Technology · 2025
H. Mahajan, Suvodeep Pyne, Anshul Pathak · Journal of International Crisis and Risk Communication Research · 2025
Saad Noori Alhamdany, M. Shakir, Mohammed Noori Alhamdany · 2025 3rd International Conference on Business Analytics for Technology and Security (ICBATS) · 2025
Abayomi Titilola Olutimehin · Asian Journal of Research in Computer Science · 2025
A. Y. Balogun, Olufunke Cynthia Metibemu, Abayomi Titilola Olutimehin · Journal of Engineering Research and Reports · 2025
Abayomi Titilola Olutimehin, Adekunbi Justina Ajayi, Olufunke Cynthia Metibemu · Journal of Engineering Research and Reports · 2025
Abayomi Titilola Olutimehin · Asian Journal of Research in Computer Science · 2025
Related research
- Risk Asessment and Thromboprophylaxis for Venous Thromboembolism (VTE) in the Antenatal Population in a Tertiary Health Facility in Nigeria — shares topic coverage
- Health Risk Assessment of Selected Trace Metals in Edible Vegetables Grown in Obudu Urban Area of Cross River State Nigeria — shares topic coverage
- Quantitative Assessment of the Risk Associated to Bacillus cereus Group for the Attieke Consumer in Daloa City (Côte d’Ivoire) — shares topic coverage
- Risk and Toxicity Assessments of Heavy Metals in Tympanotonus fuscatus and Sediments from Iko River, Akwa Ibom State, Nigeria — shares topic coverage
- Human Health Risk Assessment of Crude Oil Polluted Soil, Surface & Groundwater Sources in Emohua, Rivers State, Nigeria — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
23
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.