One of the outstanding goals of tax administrations is to sustain the continuous improvement in collected revenue. Of course, there are other subsidiary objectives, such as ensuring compliance with tax laws and improving taxpayers’ customer service satisfaction. Unarguably, the more revenue tax administrations funnel into government coffers, the better their performance. Gratuitously, there is ample headroom for expanded revenue exploitation in most West African countries. Compliance levels remain low, beckoning the innovation of tax administrations to do the needful. Citizens also expect the managers of these funds to provide good governance, which should further incentivize taxpayers to perform as expected. But good governance depends mainly on the size of tax revenue receipts and the prudence of its managers. Tax administrations can reasonably determine the size of potentially collectable yields by carefully studying historical trends in such and kindred parameters. They also try to understand compliance behaviour using several analytics techniques to process quantitative and qualitative data inputs.
In short, predictive analytics for tax administrations enable the realization of the core goals of tax administrations, such as revenue expansions, taxpayer satisfaction, and evasion reduction, leveraging an enhanced understanding of patterns of compliance behaviour. Such understanding enables the development of intelligent and targeted compliance enforcement approaches. Progressive tax administrations always design and implement various compliance and customer service programmes to change behaviour among taxpayers. In other words, the better the deployment of data analytics in realizing these goals, the more the capacity to create value and enhanced quality of life for the citizens. Fortunately, the mainstreaming of data analytics in targeting such outcomes comes with considerably reduced costs and higher levels of precision relative to alternative options.
Low compliance levels are easily traceable to poor data intelligence. Data intelligence is the next most crucial option for enhancing taxpayer compliance, assuming that the justice system is efficient. To understand taxpayer behaviour, tax administrations must also gather information concerning them. Such historical information must emanate from well-managed, interlinked and properly referenced databases. The more analytical data processing, the more insights and predictions concerning taxpayers and revenue opportunities are possible. Such database intelligence enables a global view of citizens’ sizes and their other demographic compositions capturable within the tax net. It also provides tax administrations with credible information for use by law enforcers in ensuring improved compliance. Tax administrations, for instance, find it much easier to bring corporate organizations into the net because of the availability of such data on parameters like their business registration status, income, location, and staff strength. On the contrary, the substantial absence of such information in the informal sector makes taxing them more challenging.
Therefore, data analytics has a solid place in the tax administration’s effectiveness and performance. For instance, predictive analytics for tax administrations may ask questions such as: how do we better allocate auditing budgets among different tax types? Which taxpayers have higher audit priorities? What level of compliance risks do taxpayers in a particular sector in a particular location pose compared to those in other locations? Such everyday questions for forward-looking tax administrations are effortlessly resolvable through the deployment of predictive analytics. The opportunities and attendant benefits get larger by mainstreaming analytics as part of the tax administration’s operational culture. Such a culture would ensure that the workforce in tax administration is conversant with a variety of analytical techniques usable in resolving some of those posed questions and other related puzzles. The demand for analytics within tax administrations is simply a demand for a management culture in which reliance on data is key to achieving high-performance and managerial efficiency. Fortuitously, that should be the goal of every tax administration worth its salt.
One of the beauties of such adoption is the efficient cost implications. Analytics relies on three essential foundations, which crashes the costs related to its advantages. This tripod includes the computer machine, the data, and applicable techniques fashioned as computer applications and software. Let us assume that the tax administration wants to determine the strength and direction of the relationship between taxpayers’ compliance behaviour in the two locations. The approach would be to collect relevant data on taxpayer compliance behaviour from the two locations and probably launch correlational studies using statistical software loaded into a computer. The implication is that once the data is available in the database, making such comparisons and the required predictions can be accomplished within a couple of minutes and at no extra cost. Compare this cost efficiency with accomplishing the same task and gaining the same insights using approximately four workforce members. In general, predictive analytics enables tax administrations to put a tab on the growth of operational costs while still achieving the desired results.
But tax administrations always face the challenge of obtaining such intelligence and more accurately predicting taxpayer behaviour and the magnitude and direction of tax administration performance parameters. A few areas where data analytics have become critical in tax administrations are understanding and predicting compliance behaviour and unparalleled efficiency in non-compliance detection. There is no better way to obtain red flags critical to identifying non-compliant taxpayers than relying on robust analytical processing of relevant data. Predictive analytics in tax administration also enables a more evident appreciation of the trends in target outcome areas. A rigorous trend study enables robust behavioural pattern detection, which is key to productivity and early intervention. Data analytics makes it easier for tax administrations that make good use of it better to understand the critical patterns for most optimal decision-making. One of such areas is forecasting the size of tax revenue itself, which is vital for the government’s project and program planning. Governments who know in advance the size of realizable revenue will most likely plan better than without such knowledge.
On the last note, the level of analytical skills in tax administrations within the subregion is undoubtedly shallow. The good news is that the workforce in most of these tax administrations has large numbers of knowledgeable and committed people willing to learn it. Again, some tax administrations are far more advanced than others, suggesting that analytics knowledge sharing and technical capacity exchanges may be promising strategies for tackling the challenge. WATAF may adopt that approach to achieve meaningful results in the subregional tax administrations’ analytics skill improvement agenda.
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