Artificial Intelligence, Digitalization Investment and Tax Revenue Yield in Nigeria

Abstract

The prevailing scenario in Nigeria’s tax system reveals an intricate web of challenges such as manual processes, bureaucratic bottlenecks, and inconsistent data management which contribute to revenue leakages and tax evasion. The study evaluated the effect of disruptive technology on tax revenue yield proxy with tax collection efficiency (TCE) and tax gap ratio (TGR), while exploring the moderating effect of digitalization
investment in Nigeria. Survey research design was adopted. The population comprised 14,219 tax officers of the State Internal Revenue Services (SIRS) in the six geopolitical zones in Nigeria in the administrative and management cadre as at 31st December, 2024.
A sample of 399 staff was selected using Taro Yamane (1967) formula. Data were obtained from primary sources using a structured questionnaire administered to the respondents. The obtained data were analysed using descriptive statistics and panel corrected standard error regression. The result shows that artificial intelligence proxies; AI-Powered Chatbots has positive and significant effect on TCE, and positive but insignificant influence on TGR; AI learning algorithm also reveal positive and statistically significant effect on TCE, and positive but insignificant effect on TGR; while, AI predictive analytic presents positively and statistically significant effect on both TCE and TGR. Furthermore, digitalization investment shows positive and significant
moderating effect on the link between artificial intelligence and tax revenue yield. The conclusion of the study is that the integration of artificial intelligence in tax matter is a rewarding initiative to improve tax revenue drive and generation in a developing economy. It is recommended that tax policy makers, particularly in less technology
driven economies, should maximize the potential of incorporating new digital invention through artificial intelligence to enhance tax system, while prioritizing tax payer acceptance.

Keywords: artificial intelligence, digitalisation investment, tax collection, tax gap ratio, tax revenue