Artificial intelligence, tax revenue and tax compliance in Nigeria
DOI:
https://doi.org/10.33003/fujafr-2026.v4i3.391.480-495Keywords:
artificial intelligence, machine learning, natural language processing, tax revenue, tax complianceAbstract
Purpose: This study investigated the transformative impact of artificial intelligence on tax revenue and tax compliance in Nigeria specifically leveraging on the use of Machine Learning (ML), Natural Language Processing (NLP), Blockchain, Big Data Analytics (BDA) and Robotic Process Automation (RPA).
Methodology: The study covered tax professionals from the Chartered Institute of Taxation of Nigeria (CITN) who are staff of the Nigeria Revenue Service (NRS). Data was analysed using ordinary least square regression (OLS) and ordered logistics regression.
Results and conclusion: The result from the findings show that ML, NLP and BDA positively influence tax revenue, while ML, NLP and RPA have significant and positive impact on tax compliance. BDA has a negative impact on tax compliance and blockchain has no significant influence on tax revenue and tax compliance. The study concludes that the Nigerian government and tax authorities should consider investing in artificial intelligence (AI) based infrastructure and training for tax professionals in order to harness the benefits.
Implications of findings: The study recommends the Nigerian government and tax authorities to engage with international organizations and experts who have embraced AI in their tax process to share best practices and experiences in managing the influence of AI on tax administration. This can help Nigeria stay ahead of the curve and ensure that the country is not left behind in the quest for digital revolution.
References
Adekanmbi, A. M., Shallie, A. D., & Olaniyi, O. A. (2022). Tax revenue and sustainable development in Nigeria: A disaggregated analysis. Global Journal of Arts, Humanities and Social Sciences, 10 (3), 43-54. https://www.eajournals.org/
Adelekan, O. A., Adisa, O., Ilugbusi, B. S., Obi, O. C., Awonuga, K. F., Asuzu, O. F., & Ndubuisi, N. L. (2024). Evolving tax compliance in the digital era: A comparative analysis of AI-driven models and block chain technology in US tax administration. Computer Science & IT Research Journal, 5(2), 311-335.
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), 3831-3837.
Aina, A. T. (2024). The perception of Nigerian tax officers, taxpayers, and tax consultants on the adoption of artificial Intelligence in tax risk management. Federal University Dutsin Journal of Accounting and Finance Research, 2 (4), 2992-2704.
Akinrinola, O., Addy, A. W., Ajayi-Nifise, A. O., Odeyemi, O., & Falaiye, T. (2024). Application of machine learning in tax prediction: A review with practical approaches. Global Journal of Engineering and Technology Advances, 18(02), 102–117.
Aladebumoye, T. (2025). The role of AI in enhancing tax transparency and reducing evasion. World Journal of Advanced Research and Reviews, 25(01), 206-212.
Alam, I. (2023). Exploring the potential and challenges of AI and blockchain integration for revenue enhancement in Pakistan: A comprehensive policy analysis. Khyber Journal of Public Policy, 3(2).
Anomah, S., Ayeboafo, B., Aduamoah, M., & Agyabeng, O. (2024). Blockchain technology integration in tax policy: Navigating challenges and unlocking opportunities for improving the taxation of Ghana’s digital economy. Scientific African 24, 02210.
Atadoga, S., Chinonyerem, C. A., Ajibade, S. B., Modinat, A., & Opeyemi, S. R. (2025). Blockchain and tax administration: Disruptive innovations for compliance and revenue collection. Asian Journal of Advanced Research and Reports 19 (11):76–87.
Bankole, A. R., Osamor, I. P., & Bamgboye, A. E. (2025). Effects of artificial intelligence on tax administration in Lagos State. Journal of Accounting and Management, 15(2), 144-160.
Besigomwe, K. (2025). The impact of digital platforms on taxation in Uganda: Challenges and opportunities for the Uganda revenue authority. Cognizance Journal of Multidisciplinary Studies, 5(1), 85-104.
Buckley, P., Doyle, E., McCarthy, B., & Gilligan, R. (2022). Artificial intelligence and the tax practitioner. Journal of Tax Administration, 7, (2).
Bundala, M. R., & Mjema, E. A. (2025). The influence of artificial intelligence on improving tax collection in Zanzibar revenue authority. African Journal of Empirical Research, 6 (4), 187-195.
Chen, J., Wang, M., & Sun, T. (2025). Intelligent tax systems and the role of natural language processing in regulatory interpretation. American Journal of Machine Learning, 6(4), 74-94.
Cyril, U. M., Ejike, K. C., & Udeh A. J. (2025). Digital tools, better taxes: How AI and blockchain are shaping modern tax systems. World Journal of Innovation and Modern Technology, 9 (5). http://www.iiardjournals.org/
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319.
Ehima, E., & Amede, O. F. (2024). Taxpayer’s knowledge and voluntary tax compliance amongst self- employed individuals in Edo State, Nigeria. African Development Finance Journal 7(4), 132-152.
Emelumba, P. O. & Effiong, S. A. (2026). Information technology and tax administration in Cross River State, Nigeria: Empirical evidence from a quantitative study. FUDMA Journal of Accounting and Finance Research, 4(2), 203-215.
Ezeife, E. Kokogho., E. Odio, P. E., & Adeyanju, M. O. (2021). Managed services in the U.S. tax system: A theoretical model for scalable tax transformation. International Journal of Social Science Exceptional Research, 1(1), 73-80.
Faúndez-Ugalde, A., & Mellado-Silva., R. (2023). Use of robotic process automation for tax administrations and its impact on human rights, 12, 65457. http://10.0.20.234/0719-2584.
Faúndez-Ugalde, A., Mellado-Silva, R., & Aldunate-Lizana, E. (2020). Use of artificial intelligence by tax administrations: An analysis regarding taxpayers’ rights in Latin American countries, Computer Law and Security Review, 38,105441.
FIRS (2025). Federal inland revenue service. https://firs.gov.ng/
Folorunso, A. P., & Nwankwo, C. B. (2024). Harnessing the power of AI for taxation: Nigeria’s path to improved revenue generation and transparency. Nnamdi Azikiwe University Awka Journal of Private Property Law, 1(2).
Ghosh, M., & Thirugnanam, A. (2021). Introduction to Artificial Intelligence. http://10.0.3.239/978-981-16-0415-7_2
Goyal, A. (2025). The impact of artificial intelligence on taxation: The role of AI and key use cases. International Journal of Science and Research (IJSR), 14(5), 1213-1220.
Hussain, H. T, Musa, B. S., & Musa, S. J. (2024). Tax revenue and economic growth in Nigeria. Journal of Applied Philosophy, 22 (3), 36-59. http://10.0.51.84/RG.2.2.30799.70560
Ideh, A. O. (2019). Tax revenue and economic development of the Nigerian economy. Nigerian Journal of Management Sciences, 7(1), 222-231. https://www.bsum.edu.ng/njms/journals/vol7no1/files/file24
IMF (2021). Fiscal monitor: Managing public wealth. International Monetary Fund.
Judijanto, L., Budiaji, I.W., & Fkun, E. (2024). Blockchain technology and the future of tax administrations: Opportunities and risk. Prosiding Seminar Nasional Indonesia, 2 (2), 299-310.
Kuphanga, D. (2024). Questionnaires in research: Their role, advantages, and main aspects.
Martinez, A. L. (2025). Artificial Intelligence in tax administration: Enhancing compliance, transparency, and ethical governance.
Munjeyi, E. & Schutte, D. (2025). Citizens' perspectives of AI adoption in taxation and its effects on long-term voluntary tax compliance and socio-cultural norms in Botswana. African Journal of Inter/Multidisciplinary Studies, 2(2), 1-15.
Naik, A. & Nandakumar, A. (2025) Tax administration and domestic revenue in the digital era. ODI Global Report. London. http://odi.org/publications/public-finance-in-the-digital era emerging themes
NRS. (2026). Nigeria Revenue Service.
NTAA. (2025).Nigeria tax administration.
Obaretin, O. & Ohonba, N. (2018). Tax revenue and economic growth in Nigeria. Accounting and taxation review, 2 (1), 16 - 23.
OECD. (2025). Comparative Information on OECD and other Advanced and Emerging Economies.
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), 503-13.
Olorunfemi, G. S., & Odewole, O. B. (2025). Artificial intelligence (AI) and petroleum profit tax administration in Nigeria economy. FUDMA Journal of Accounting and Finance Research, 3(3), 2992- 2704.
Olumoh, Y.A. (2024). Tax administration practices and compliance: Evidence from Kwara State, Nigeria. Federal University Dutsin Journal of Accounting and Finance Research, 2(4), 17-30.
Omolere, O. G., & Onyemechi, C. G. (2024). The role of AI and machine learning in optimizing taxation policies for rural development and infrastructural upgrades. International Journal of Research Publication and Reviews, 5(10), 4397-4412.
Opeyemi, A. O., Ewim, S. E., Bulya, N. J. S., & Ogundipe, T. (2024). Leveraging AI-Driven financial literacy and tax compliance solutions to foster economic growth in underserved communities and small businesses. International Journal of Advanced Economics 6 (10). http://10.0.201.138/ijae.v6i10.1668
Overbye-Thompson, H., Hamilton, K. A. (2025). A diffusion of innovations measurement scale for reinvention, relative advantage, compatibility, complexity, trialability and observability.
Oyedokun, G. E., & Ayinde, O. A. (2023). Digitalization, culture, and taxpayers’ compliance in Nigeria. Journal of Economics, Finance and Management Studies, 6(12), 5888-5896.
Pamisetty, V. (2022). AI-powered decision support systems for enhancing tax compliance and public revenue management. International Journal of Science and Research, 11(12), 2319-7064.
Pamisetty, V., Pandiri, L., Singireddy, S., Annapareddy, V. N., & Sriram, H. K. (2022). Leveraging AI, machine learning, and big data for enhancing tax compliance, fraud detection, and predictive analytics in government financial management. Migration Letters, 19 (S5), 1770-1784. http://www.migrationletters.com/
PLoS One 20 (10), e0334616.
PWC. (2025). The Nigerian tax reform acts. https://www.pwc.com/ng/en/publications/the-nigerian-tax-reform-acts.html
Qu, G., & Jing, H. (2025). Is new technology always good? Artificial intelligence and corporate tax avoidance: Evidence from China. International Review of Economics and Finance, 98, 103949.
Titcombe, D., & Micah, L. C. (2026). Artificial intelligence and tax administration in Nigeria. Global Publication House International Journal of Social Science and Humanities Research, 9(01), 206-229.
World Bank. (2022). Nigeria economic update: Boosting revenue through the informal sector. https://www.worldbank.org/
Yayman, D. (2021). Blockchain in Taxation. Journal of Accounting and Finance, 21(4).
Zaqeeba, N., Alqudaha, H., Alshira’hb, A.F., Lutfic, A., Almaiahg, M. A & Alrawadh, M. (2024). The impact of using types of artificial intelligence technology in monitoring tax payments. International Journal of Data and Network Science, 8.
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