AI-Driven VAT Gap Closure for Nigeria's Digital & Informal Economy

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@article{1711902,
      author = {Blessing Chidiebere Nwobia, Dr. Lawrence C. Azike.},
      title = {AI-Driven VAT Gap Closure for Nigeria's Digital & Informal Economy},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {2},
      pages = {362-392},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711902.pdf},
      abstract = {This study examines the application of artificial intelligence technologies in closing the Value Added Tax (VAT) gap within Nigeria's burgeoning digital and informal economy sectors. Nigeria's VAT collection efficiency remains suboptimal, with significant revenue leakage attributed to the expansive informal sector and rapidly growing digital economy. This research investigates how AI-driven solutions including machine learning algorithms, predictive analytics, and automated compliance systems can enhance tax administration effectiveness. Using a mixed-methods approach combining quantitative analysis of tax data and qualitative interviews with tax administrators, the study reveals that AI implementation could potentially reduce the VAT gap by 35-42% over a five-year period. The findings indicate that machine learning models achieve 87% accuracy in identifying non-compliant businesses, while automated systems reduce processing time by 64%. However, implementation challenges include infrastructural deficits, data quality issues, and institutional capacity constraints. The study contributes to the limited literature on AI applications in developing economy tax systems and provides actionable recommendations for policymakers seeking to modernize revenue collection frameworks.},
      keywords = {Artificial Intelligence, VAT Gap, Tax Administration, Digital Economy, Informal Sector, Nigeria, Machine Learning, Revenue Collection, Tax Compliance, Predictive Analytics},
      month = {August},
      doi = {https://doi.org/10.64388/IREV5I2-1711902}
  }