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Effect of Advanced Analytics and Artificial Intelligence on Tax Administration Performance in Lagos State, Nigeria

Adeleke, Saheed Abiodun, Adedipe Oluwaseyi Ayodele Ph.D

Abstract

Nigeria’s tax to GDP ratio remains one of the lowest compared to peer economies, thereby sustaining fiscal reliance in volatile oil revenue and external borrowing. Despite the increasing digitalization of subnational revenue authorities, there is little evidence (if any) that use of advanced analytics and Artificial Intelligence (AI) results in measurable improvements in administrative performance. This paper examined the effect of advanced analytics and artificial intelligence on tax administration performance in Lagos state, Nigeria. The survey research design was employed for this study. The population of the study consists of 528 managements, strategic and departmental staff of Lagos State internal revenue service. A sample size of 223 was determined using the Krejcie and Morgan (1970) formula, and was drawn through stratified random sampling from the Information and Communication Technology Unit, Compliance and Enforcement Group, Tax Operations Group, Strategic Planning and Research Department. The use of structured questionnaire study yielded 190 questionnaires as valid which was 85.2 per cent. A very significant positive contribution to revenue collection performance through the use of advanced analytics was found to exist (β = 0.514, R2 = 0.465, p < 0.001). Also, the adoption of artificial intelligence (AI) was found to significantly improve tax compliance rates (β = 0.541, R2 = 0.413, p < 0.001). The use of robust and reputable AI in selecting audits greatly boosts the chances of success (with β = 0.608, R2 = 0.501, p < 0.001). All three technologies together explained 60.0 per cent of the taxi administration performance (adjusted R2 = 0.600, F (3, 186) = 95.63, p < 0.001), with the strongest relative influence exerted by AI-driven audit selection. The paper concludes that enforcement enabled by analytics of taxes rather than deployment of generalized AI produces the highest returns in performance for revenue authorities that are constrained in resources. It suggests prioritizing the funding of the AI risk-scoring system plus automated case selection application, expediting the integration of taxpayer data across revenue-generating agencies, and the establishment of a formal governance framework for AI. Key words: Advanced analytics, artificial intelligence, audit selection. tax administration performance JBAE

Keywords

Advanced analyticsartificial intelligenceaudit selection. tax administration performance JBAE

References

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