PAPER 21/02/2025
Pakistan’s Federal Board of Revenue (FBR) faces persistent challenges in tax collection, with the country’s tax-to-GDP ratio stagnating at 10%—well below the global average of 15–20%. This shortfall is worsened by a largely informal economy (comprising nearly 70% of economic activity), widespread tax evasion, and underemployment. Addressing these structural inefficiencies is critical, as FBR plays a central role in ensuring fiscal stability and advancing Pakistan’s economic development agenda.
Artificial Intelligence (AI) presents a transformative opportunity to enhance tax compliance, detect fraud, and improve revenue forecasting. FBR has long struggled with systemic inefficiencies, tax evasion, and a narrow tax base. However, by applying AI-driven technologies such as predictive analytics, machine learning, and automation, Pakistan can significantly expand its tax net, increase compliance, and boost revenue collection. Implementing AI solutions could potentially double the country’s tax-to-GDP ratio within five years, strengthening transparency, efficiency, and public trust in the tax system.
This paper explores the benefits of AI integration in tax administration while addressing key implementation challenges, including data availability, technological infrastructure, and cybersecurity risks. It concludes with actionable recommendations for modernising FBR, positioning it as a technology-driven authority capable of addressing Pakistan’s economic challenges through innovation and efficiency.