ARTIFICIAL INTELLIGENCE, TECHNOLOGY INFRASTRUCTURE AND TAX EVASION IN EMERGING ECONOMY
Main Article Content
Muyiwa Emmanuel Dagunduro
Gbenga Ayodele Falana
Oluyinka Isaiah Oluwagbade
Niyi Solomon Awotomilusi4
Akinyemi Wumi Ogunleye
Muideen Adeseye Awodiran
Adebola Abass Jabar
Tax evasion is a global problem that costs governments billions of dollars in lost revenue every year. To address these issues, this study investigated the effect of artificial intelligence on tax evasion in Nigeria. This study specifically examined how machine learning, natural language processing, intelligent decision support systems, and expert systems, when supported by a strong technology infrastructure, might reduce tax evasion and improve revenue collection. This study used a survey research approach, with main data acquired using a well-structured questionnaire. The target demographic consisted of 79 Federal Inland Revenue Service (FIRS) employees in Ikeja Lagos, who specialized in artificial intelligence. A random sampling technique was used to ensure a representative sample, reducing selection bias and increasing the generalizability of the findings. The acquired data was examined using descriptive statistics and multiple regression analysis. The study discovered that machine learning and natural language processing considerably minimize tax evasion, but their effectiveness is limited by robust technological infrastructure, which improves fraud detection but reduces their impact. While expert systems significantly reduce tax evasion, they may be abused when technology infrastructure improves, but intelligent decision support systems had no meaningful impact, showing limitations in their current use in tax enforcement. This study concluded that AI technologies such as machine learning, natural language processing, and expert systems should be strategically integrated alongside well-regulated technological infrastructure to maximize fraud detection capabilities while minimizing the risk of misuse. This study suggested that tax authorities invest in machine learning-driven automation to detect fraud and monitor tax compliance.
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