The influence of artificial intelligence on improving tax collection in Zanzibar Revenue Authority
Keywords:
Artificial Intelligence, Tax Fraud Detection, Tax Collection, Zanzibar Revenue AuthorityAbstract
This research was conducted to explore the impact of Artificial Intelligence (AI) on improving tax collection practices within the tax collection agency known as the Zanzibar Revenue Authority (ZRA). The study aligned with two specific objectives: first, to assess employees’ perceptions of AI on improving tax collection at ZRA, and the second objective is to examine the role of AI in tax fraud detection on improving tax collection at ZRA. The study employed a quantitative research methodology with a cross-section research design. The Technological Acceptance Model (TAM) was the model which was used to support this study. The target population was 373 employees who are engaged in tax collection practices, with a sample size of 193, which was obtained through Yamane’s formula, and the proportionate stratified sampling strategies are used to select the participants. Closed-ended questionnaires were used to collect primary data. Data analysis was performed using descriptive and inferential statistics through multiple linear regression analysis. The results showed that there is a positive relationship between the employees’ perception and improved tax collection (r=0.827, p<0.1). This strong positive perception indicates a readiness and supportive environment to implement AI in tax administration, which can help to streamline processes and increase operational efficiency. The results also showed that there is a positive relationship between the AI role in tax fraud detection and improved tax collection (r=0.849, p<0.1). This indicates that AI has the potential to increase revenue and help the taxpayers to comply with tax regulations effectively. Since ZRA employees perceived AI as useful tools to improve tax collection, they are ready to support it. It is recommended that ZRA urgently needs to invest in AI to help improve revenue performance; this investment will require a robust digital infrastructure with strong data security and privacy measures while adopting a phased implementation strategy. To avoid failure, it is advisable to start with pilot projects while carefully selecting suitable AI technologies for successful integration with existing systems.
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Copyright (c) 2025 Maryam Ramadhan Bundala, Prof. Emanuel Mjema

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