Artificial Intelligence (AI) and Petroleum Profit Tax Administration in Nigeria Economy
DOI:
https://doi.org/10.33003/fujafr-2025.v3i3.215.129-142Keywords:
Artificial Intelligence (AI), Petroleum Profit Tax, Tax Administration, Data Analytics, ComplianceAbstract
This study examines the impact of Artificial Intelligence (AI) adoption on Petroleum Profit Tax (PPT) administration in Nigeria. The study looks into the level of AI deployment, the types of AI technologies now in use, their perceived efficacy, simplicity in usage, associated problems, and their impact on tax compliance and revenue collection. Data gathering was done with 98 individuals, who are officers in tax agencies, compliance officers, and AI professionals. Descriptive statistics and multiple regression analysis were used to assess the link between AI adoption and important tax outcomes. The findings showed that, while overall AI adoption is limited, techniques like machine learning, predictive analytics, and robotic process automation are becoming more popular. AI was discovered to greatly boost compliance efforts, improve the accuracy of tax reporting, and positively affect revenue predictions and audit results. The regression findings demonstrated that AI-powered data analytics and reporting systems had a statistically significant impact on PPT income production and tax compliance. However, constraints such as a lack of technical skills, aversion to change, and expensive implementation costs remain important obstacles. The study further discovered that when effectively applied and supported, AI will change tax administration. It suggests capacity building, enhanced infrastructure, data governance, and regulatory reforms to optimize the use of artificial intelligence in tax operations within Nigeria's petroleum sector.
References
ACCA. (2021). Financial reporting (FR) study text. Association of Chartered Certified Accountants.
Adebayo, A., & Okeke, J. (2020). Petroleum profit tax administration and revenue generation in Nigeria. International Journal of Accounting and Taxation, 8(1), 45–56.
Adewale, O., Chukwuma, J., & Musa, A. (2021). Barriers to Artificial Intelligence adoption in Nigerian public institutions. African Journal of Public Administration, 9(2), 102–118.
Aondover, T. (2025). Underreporting practices in Nigeria’s petroleum sector: Challenges and solutions. Nigerian Journal of Energy Economics, 7(1), 55–70.
Alexander, D., Britton, A., & Jorissen, A. (2017). International financial reporting and analysis (6th ed.). Cengage Learning.
Becker, G. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169–217. DOI: https://doi.org/10.1086/259394
Bhatia, H. L. (2001). Public finance (25th ed.). Vikas Publishing.
BPP Learning Media. (2022). Financial reporting (IFRS). BPP Publishers.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Central Bank of Nigeria. (2022). Statistical bulletin. https://www.cbn.gov.ng/
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. DOI: https://doi.org/10.2307/249008
Elijah, M., Karanja, S., & Wanjiru, T. (2021). Robotic process automation and tax audit efficiency in Kenya. Journal of Tax Administration and Policy, 5(1), 11–22.
Epstein, L., Nach, R., & Bragg, S. (2014). IFRS: Interpretation and application. Wiley.
Eze, A., & Onyekachi, E. (2020). Challenges of petroleum profit tax collection in Nigeria: The way forward. Journal of Energy Policy and Management, 12(4), 30–42.
Ibe, C. (2018). Petroleum profit tax administration and compliance challenges in Nigeria. International Journal of Business and Management Invention, 7(3), 22–30.
IASB. (2023). International Accounting Standard 37: Provisions, contingent liabilities, and contingent assets. IFRS Foundation.
KPMG. (2022). Tax technology and AI integration in Nigeria. KPMG Insights Report.
Kok, J., Mashinini, N., & Smith, L. (2020). The adoption of artificial intelligence in South African tax administration. Journal of African Taxation, 3(1), 25–38.
Musgrave, R. A. (1959). The theory of public finance. McGraw-Hill. DOI: https://doi.org/10.1007/978-1-349-23426-4
Nigerian Extractive Industries Transparency Initiative (NEITI). (2022). NEITI 2021 Oil and Gas Industry Report. https://www.neiti.gov.ng/
Nwankwo, O., & Okafor, K. (2021). Technological gaps in petroleum profit tax collection in Nigeria. Journal of Accounting and Financial Studies, 9(1), 59–73.
OECD. (2021). Tax administration 2021: Comparative information on OECD and other advanced and emerging economies. OECD Publishing. DOI: https://doi.org/10.1787/cef472b9-en
Oladipo, T. (2020). Regulatory risks in artificial intelligence adoption in Nigeria’s public sector. Nigerian Journal of Governance and Development, 4(2), 50–61.
Okoye, A., & Onyeka, E. (2022). Machine learning and predictive analytics in revenue mobilization: Evidence from Ghana. African Journal of Taxation Studies, 4(3), 77–90.
Phillips, R., Ibrahim, J., & Omoniyi, A. (2024). Artificial intelligence and fiscal transparency in Nigeria’s oil sector. African Journal of Energy Policy, 10(1), 15–32.
PPTA. (2004). Petroleum Profits Tax Act. Federal Government of Nigeria Official Gazette.
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson.
Sani, U., Abdulrahman, A., & Okafor, N. (2024). AI applications in public sector taxation in West Africa. West African Journal of Fiscal Studies, 8(1), 33–48.
World Bank. (2020). Harnessing digital technology for tax revenue mobilization in developing countries. World Bank Policy Note.
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