Background of the Study
Big data analytics has become an invaluable tool in shaping tax policies worldwide. By analyzing large datasets, tax authorities can identify patterns, forecast revenues, and detect fraudulent activities. In Nigeria, the Federal Ministry of Finance has begun leveraging big data analytics to address inefficiencies in tax administration and optimize policy-making. This approach enables better compliance monitoring, enhanced revenue projections, and tailored tax policies (Oluwole & Adeyemi, 2024).
However, challenges such as inadequate infrastructure, limited technical expertise, and data privacy concerns hinder the full adoption of big data analytics in Nigeria’s tax system. This study examines how big data analytics is applied by the Federal Ministry of Finance to optimize tax policy and explores ways to address existing challenges.
Statement of the Problem
Despite its potential, the application of big data analytics in Nigeria's tax policy remains underutilized due to infrastructural deficits, poor data integration, and a lack of skilled personnel. These limitations prevent the Federal Ministry of Finance from fully realizing the benefits of data-driven decision-making, resulting in suboptimal tax policies and revenue leakages (Ekwueme & Ibrahim, 2023).
This study investigates the role of big data analytics in optimizing tax policies, identifying barriers, and proposing strategies to enhance its adoption and effectiveness in Nigeria.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
The study focuses on the Federal Ministry of Finance's use of big data analytics in tax policy optimization between 2020 and 2025. Limitations include data availability constraints, potential bias in government-provided information, and the nascent nature of big data adoption in Nigeria.
Definitions of Terms
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