THE ROLE OF ARTIFICIAL INTELLIGENCE IN REVENUE MANAGEMENT IN ZIMBABWE

THE ROLE OF ARTIFICIAL INTELLIGENCE IN REVENUE MANAGEMENT IN ZIMBABWE

KEITH TICHAONA TASHU1

TAFADZWA MOYO

SYLVESTER MARUMAHOKO

The government of Zimbabwe has continued with the economic management of protectionism since independence in 1980, which was being used by the former colonial government. The new black government embarked on huge expenditure on capital (infrastructure) such as roads, schools and hospitals and this drove up government spending against government revenue. Despite efforts to optimise revenue, the nation’s socio-economic conditions have worsened and are marked by a high inflationary environment, low utilisation of industrial capacity and decreases in Gross Domestic Product (GDP). The financial flexibility is limited due to weak tax revenue performance amidst increasing current spending and a shrinking tax foundation. Poor revenue collections from the formal economy have compelled the government to resort to the taxation of the informal economy, but the big challenge is that the government seems reluctant to embrace technology for the taxation in this sector, yet technology has already advanced such that developing countries are already embracing emerging technology like Artificial Intelligence (AI) and Machine Learning (ML) in revenue management. The research aims to evaluate the revenue structure in Zimbabwe, challenges affecting revenue generation in Zimbabwe and the role of AI in revenue optimisation. The research made use of extant qualitative research methods. Research findings indicate that revenue generation in Zimbabwe is mostly affected by complex tax system, capacity constraints, smuggling, corruption, low tax morale, inadequate information and inadequate checks and balance. The role of AI in revenue optimisation includes revenue forecasting, assessing economic conditions, real time policy adjustments, detecting fraud and corruption, identifying tax inefficiencies and optimizing resource allocation. It can be concluded that, by employing AI-driven predictive models, the government can allocate tax revenue more precisely to fund infrastructure projects, such as healthcare facilities, schools and roads, ultimately improving living standards and economic outcomes in underdeveloped regions.

ZIMBABWE JOURNAL OF BUSINESS, ECONOMICS AND MANAGEMENT

2025