Analytics for Tax Compliance in Public Revenue Systems
Keywords:
Data; Tax compliance monitoring; Public revenue systems; Decision support; Policy evaluation; Policy design; Administration arrangements; Policy instruments; Tax gaps; Revenue risk.Abstract
This study presents evidence-based analysis of data-driven tax compliance monitoring within public revenue systems, emphasizing rigorous methodology, transparent reporting, and measurable outcomes. Emerging trends in the digitalization of the economy and public service delivery increasingly motivate the use of big data analytics by tax authorities for efficient revenue collection, including compliance monitoring. Political pressure or judicial rulings often push decision-making and applied analytics into unsafe territories. In contrast, peer-reviewed modelling and analysis support legitimate and transparent data-driven compliance monitoring. Digital economy taxation gap analytics is one area gaining widespread research and implementation attention. Another core aspect of tax compliance risk assessment is the detection of tacit and explicit noncompliance. Risk-assessment support for audit selection or the identification of high-risk sectors and segments is increasingly provided by data-analytical methods.
While demonstrated in a financial-supply-chain context, the approach is transferable beyond the scope and context of any one implementation. Empirical examinations validate the proposed concepts, metrics, and models, establishing the methodological foundation for a broader application across public revenue systems. Supply-side data companies maintain data about every firm across digital supply chains. Cross-jurisdictional public-sector data-sharing silos constitute uncontested evidence of tax gap size and sector non-compliance risk or willingness summaries. Non-participation in regulatory audits, data-feedback mechanisms, and information-exchange strategies results in an ever-increasing compliance gap. Tax administrations and regulators have decision responsibility and operational authority for compliance governance, yet any failure to embrace any one of the risk-and-remediation strategies should also be held to account.
References
1. Alexopoulos, A., Dellaportas, P., Gyoshev, S., Kotsogiannis, C., Olhede, S. C., & Pavkov, T. (2021). A network and machine learning approach to detect value added tax fraud. arXiv.
2. Tualeka, S. H., Rokhimakhumullah, D. N. F., & Ningsih, D. N. C. (2021). Peran penggunaan teknologi informasi terhadap ketertiban administrasi keuangan dan kepatuhan perpajakan pada digital business. Jurnal Akuntansi Aktual, 8(2).
3. Mascagni, G., Mengistu, A., & Santoro, F. (2021). Can ICTs increase tax compliance? Evidence on taxpayer responses to technological innovation in Ethiopia. Journal of Economic Behavior & Organization, 189, 172–193.
4. Kumar, K. M., Parasar, A., Walia, A., Inala, R., & Thulasimani, T. (2025, August). Enhancing Risk Management Strategies in Financial Institutions Using CNN and Support Vector Regression. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE.
5. Al-Karablieh, Y., Koumanakos, E., & Stantcheva, S. (2021). Clearing the bar: Improving tax compliance for small firms through target setting. Journal of International Economics, 130, 103452.
6. Savić, M., Atanasijević, J., Jakovetić, D., & Krejić, N. (2022). Tax evasion risk management using a hybrid unsupervised outlier detection method. Expert Systems with Applications, 193, 116409.
7. Bellon, M., Dabla-Norris, E., Khalid, S., & Lima, F. (2022). Digitalization to improve tax compliance: Evidence from VAT e-invoicing in Peru. Journal of Public Economics, 210, 104661.
8. Ramirez-Alvarez, J., Oliva, N., & Andino, M. (2022). Tax compliance and electronic invoicing in Ecuador: An impact assessment. Problemas del Desarrollo, 53(208), 97–123.
9. Kasper, M., & Alm, J. (2022). Audits, audit effectiveness, and post-audit tax compliance. Journal of Economic Behavior & Organization, 195, 87–102.
10. Okunogbe, O., & Santoro, F. (2022). The promise and limitations of information technology for tax mobilization. The World Bank Research Observer, 37(2), 295–324.
11. OECD. (2022). Tax Administration 3.0 and electronic invoicing: Initial findings. OECD Publishing.
12. Battaglini, M., Guiso, L., Lacava, C., Miller, D. L., & Patacchini, E. (2022). Refining public policies with machine learning: The case of tax auditing. National Bureau of Economic Research.
13. Nose, M., & Mengistu, A. (2023). Exploring the adoption of selected digital technologies in tax administration: A cross-country perspective. IMF Notes, 2023(008).
14. Segireddy, A. R. (2024). Machine Learning-Driven Anomaly Detection in CI/CD Pipelines for Financial Applications. Journal of Computational Analysis and Applications, 33(8).
15. Rabasco, M., & Battiston, P. (2023). Predicting the deterrence effect of tax audits: A machine learning approach. Metroeconomica, 74(3), 531–556.
16. Hebous, S., Jia, Z., Løyland, K., Thoresen, T. O., & Øvrum, A. (2023). Do audits improve future tax compliance in the absence of penalties? Evidence from random audits in Norway. Journal of Economic Behavior & Organization, 207, 305–326.
17. Kasper, M., & Rablen, M. D. (2023). Tax compliance after an audit: Higher or lower? Journal of Economic Behavior & Organization, 207, 157–171.
18. Lancee, B., Rossel, L., & Kasper, M. (2023). When the agency wants too much: Experimental evidence on unfair audits and tax compliance. Journal of Economic Behavior & Organization, 214, 406–442.
19. Alm, J., Burgstaller, L., Domi, A., März, A., & Kasper, M. (2023). Nudges, boosts, and sludge: Using new behavioral approaches to improve tax compliance. Economies, 11(9), 1–22.
20. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
21. Nose, M., & Mengistu, A. (2023). Digital technologies and tax administration: Evidence on tax collection and compliance. IMF research on tax administration digitalization.
22. Technology and tax compliance spillovers: Evidence from a VAT e-invoicing reform in Peru. (2023). Journal of Economic Behavior & Organization, 212, 756–777.
23. Does going cashless make you tax-rich? Evidence from India’s demonetization experiment. (2023). Journal of Public Economics, 224, 104907.
24. Aggarwal, S. (2024). The role of artificial intelligence in tax administration and compliance: A new era of digital taxation. Educational Administration: Theory and Practice, 30(1).
25. Olabanji, S. O., Olaniyi, O. O., & Olaoye, O. O. (2024). Transforming tax compliance with machine learning: Reducing fraud and enhancing revenue collection. Asian Journal of Economics, Business and Accounting, 24(11), 503–513.
26. Battiston, P., Gamba, S., & Santoro, A. (2024). Machine learning and the optimization of prediction-based policies. Technological Forecasting and Social Change, 199.
27. Barwahwala, T., Mahajan, A., Mittal, S., & Reich, O. (2024). Is model accuracy enough? A field evaluation of a machine learning model to catch bogus firms. National Bureau of Economic Research Working Paper No. 32705.
28. Nose, M., & Mengistu, A. (2024). Tax administration: Essential analytics for compliance risk management. International Monetary Fund Technical Notes and Manuals.
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Copyright (c) 2025 Daniel Thompson (Author)
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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
