ISLAMABAD: A meeting on broadening of tax base was informed that the Federal Board of Revenue (FBR) has provided 14 million records of financial transactions of citizens to the National Database and Registration Authority (NADRA) to compute indicative income and tax liability of non-filers by use of artificial intelligence. The meeting was presided over by Adviser to the Prime Minister on Finance Shaukat Tarin on Monday. The FBR chairman and his team gave a detailed presentation on the progress on readiness for potential taxpayer outreach initiative to boost the revenue growth and resource mobilisation. The FBR chairman apprised the adviser that steps have been initiated for compilation of data, with the support of the NADRA, which would be available to potential and current taxpayers in a presentable and comprehensible manner through a web portal. According to Business Recorder, the 14 million financial records included property transactions, vehicle purchases, registration of cars with provincial excise departments, buying/selling of movable and immovable properties, utility bills, foreign travels, and other heavy expenditures.

Tax evasion is the illegal non-payment of taxes by individuals, corporations, and trusts. It results in a loss of state revenue that can undermine the effectiveness of government policies. One measure of tax evasion is the so-called tax gap: the difference between the income that should be reported to the tax authorities and the amount actually reported. However, economists lack a robust method for estimating the tax gap through a bottom-up approach based on fiscal audits. This is difficult because the declared tax base is available on the whole population but the income reported to the tax authorities is generally available only on a small, non-random sample of audited units. This induces a selection bias which invalidates standard statistical methods. Here, we use machine learning based on a 2-steps Gradient Boosting model, to correct for the selection bias without requiring any strong assumption on the distribution. We use our method to estimate the Italian VAT Gap related to individual firms based on information gathered from administrative sources. Our algorithm estimates the potential VAT turnover of Italian individual firms for the fiscal year 2011 and suggests that the tax gap is about 30% of the total potential tax base. Comparisons with other methods show our technique offers a significant improvement in predictive performance.

Artificial Intelligence (AI) may well be the most powerful technology of the 21st century, helping to solve humanity's most complex unsolved problems: environmental, social, and more. Yet sceptics believe that AI's risks are as large as its potential benefits. How can they be avoided? And why isn't the most powerful technology being used more widely today to solve the world's greatest "wicked" problems? Great technological advances are often a double-edged sword.