With tax revenue estimates for the year of income 2019/2020 topping the Sh2.9 trillion mark for the second time running per the 2019/2020 Estimates of Revenue, Grants and Loans released by the National Treasury, we are all well aware that the Kenya Revenue Authority is facing a herculean task.
This has led to a steady push by the National Treasury and the KRA to increase tax revenues for the 2019/2020 fiscal year, as well as downsize the National Budget for the same period. This will culminate in the issuance of the Finance Bill 2019 this week – a Bill of Parliament which, inter-alia, seeks to introduce new tax measures aimed at enabling the government achieve its ambitious targets.
While these tax increases will increase KRA’s total collections for the 2019/2020 period, it is unlikely that the increase will result in KRA hitting its collection targets altogether. Consequently, KRA, and in extension the National Treasury, have committed to placing more resources into the KRA’s tax base expansion strategy.
Per the strategy, KRA seeks to leverage on advances in technology to identify new sources of taxation income, including bringing potential tax cheats under the tax net. Simply put, KRA plans to reduce tax fraud, tax evasion and tax avoidance through innovative techno-centric processes such as Big Data analytics.
This will involve, in the early stages, the linking of multiple platforms and databases with KRA’s tax administrative platform, iTax, with potential sources of data going forward being bank data and Mpesa transactional data, among others. Based on this, we can expect that KRA will pool these sources of data together, analysis the same, and derive insights into taxpayers’, be they individuals or businesses, tax compliance journey.
Through Big Data analytic processes, KRA can identify compliance anomalies as and when they occur, as well as leverage on predictive algorithms to develop modern tax-risk profiles, analyze trends and flag potential audit issues. As touted the world over, the use of Big Data analytics, coupled with Artificial Intelligence processes, has the potential to assist revenue authorities not only reduce the levels of tax fraud, thereby sealing systemic and historic tax loopholes, but further predict and prevent future instances of tax fraud.
Through the access of troves of data points, potential opportunities for the KRA going forward would be the implementation of a tax credit rating system. The ability to track a single taxpayer’s tax compliance journey will similarly enable the revenue authority maintain a dedicated tax credit rating system that will spur efficiency in the tax compliance enforcement process. For instance, tax audit resources would be directed at high risk taxpayers thereby reducing the number of audits conducted at any given period; conversely, low risk taxpayers may be rewarded with benefits and incentives to maintain their tax compliance status.
Karen Kandie – MD IDB Capital
