Searching taxpayer information from various databases and prioritising their use in demanding tax payments strengthen subnational government tax administration and proactively enhance tax compliance and revenue generation. Utilising available data sources to implement proactive tax collection measures enables tax authorities to improve their capacity to cover taxpayers comprehensively, identify potential tax evaders, and ensure a fair tax system. An upbeat tax management approach reduces reliance on taxpayer-provided information. It equally minimises errors primarily because its success is contingent on data integration, analytics, collaboration with other government agencies, and public awareness campaigns, paving the way for a more efficient and effective tax collection process. For instance, by utilising various databases and other relevant information sources, the Internal Revenue Service [IRS] have access to more comprehensive and accurate taxpayer information, minimising errors and discrepancies in tax filings and payments, leading to a more reliable and transparent tax system. Again, taking the initiative to demand tax payments based on reliable data encourages taxpayers to fulfil their obligations promptly. This approach indisputably facilitates zero tolerance for tax evasion and fosters a culture of compliance among taxpayers. It also helps reduce the burden on taxpayers by minimising the need for manual tax filing and reporting.
But, the current state of tax compliance in Nigeria is mixed. According to the World Bank’s “Doing Business 2020” report, Nigeria’s tax compliance rate stood at 52.9 percent in 2019, indicating that substantial headroom existed for business citizens’ compliance with their tax obligations. Again, as of 2021, the National Bureau of Statistics put Nigeria’s tax-to-GDP ratio at 10.86 percent, an upward revision from the previously estimated six percent. But even that estimate is significantly low relative to other countries. Challenges such as a large informal economy, weak compliance enforcement mechanisms, and a lack of awareness about tax obligations account for this low performance. Many individuals and businesses outside the formal sector do not come under the tax net, making the informal economy a significant barrier to tax compliance. Some subnational governments, such as the Lagos State Internal Revenue Service (LIRS), have launched far-reaching initiatives to improve the situation. For instance, there are guidelines for taxpayers resident in Lagos State on obtaining and generating tax clearance certificates digitally. Alongside Lagos State, many other subnational tax authorities are adopting more aggressive approaches, such as sealing up business premises of organisations, putting up non-compliance stickers, and holding principal officers of firms to public scrutiny and prosecution under the tax law for non-compliance, failure to make timely payments, or filing tax returns. At the federal level, the Nigerian government introduced the Voluntary Assets and Income Declaration Scheme (VAIDS) in 2017 to encourage voluntary compliance by providing incentives and amnesty for taxpayers to regularise their tax affairs. Additionally, the Federal Inland Revenue Service (FIRS) has strengthened its enforcement capabilities through technology and data analytics. Some subnationals are catching up as well.
Although not desirable, the current norm is that subnational tax authorities depend significantly on taxpayers to provide accurate information and file tax returns voluntarily. So, rather than proactively obtaining such data from various databases, they spend more time demanding accurate taxpayer information. This reactive approach is replete with limitations. The most apparent of them is inaccurate information. Although taxpayers may intentionally provide incomplete or inaccurate information to avoid paying large amounts, such incorrectness may be unintentional sometimes. For instance, some taxpayers may not fully understand their tax obligations or the consequences of providing inaccurate information. This unacceptable behaviour could be because of a lack of education or awareness about the relevant tax laws and regulations. The complexity of tax laws may also be a contributing factor. Many regulations and rules in tax systems create confusion and make it difficult for taxpayers to accurately report their income and deductions. It may also provide opportunities for legal tax avoidance or illegal tax evasion. Taxpayers may also provide inaccurate information out of fear of being audited or facing penalties for non-compliance. This fear can lead to under-reporting income, over-reporting of deductions, or other forms of tax evasion. Conversely, subnational tax authorities often fail to allocate significant resources to auditing and investigating non-compliant taxpayers, diverting resources from other critical tasks. Reactive tax collection approaches are time-consuming and costly and often result in delayed revenue collection.
Undoubtedly, identifying non-compliant taxpayers can be challenging. However, the deployment of data analytics has revolutionised tax enforcement strategies. Data analytics in this context involves the analysis of vast troves of data within the subnational tax authorities, including income records, property information, and transaction histories, and extracting valuable insights and patterns from them. Such analysis can easily reveal anomalies and inconsistencies in taxpayer information, enabling authorities to pinpoint potential non-compliance. For example, by cross-referencing information from various databases, data analytics reduce the risk of under-reporting or tax evasion. Again, data analytics ensures that tax authorities do not wrongly deploy the one-size-fits-all approach by issuing tax demands based on risk profiles. Data analytics also provides effective predictive modelling of citizens’ compliance, allowing authorities to take proactive measures by forecasting future compliance levels and issues. Other derivable benefits of the deployment of data analytics include a timely collection of citizens’ taxes and better resource optimization. In the former, the government proactively demands taxes to ensure convenient collection of revenue, which improves their ability to fund essential services. In the latter, focusing on high-risk taxpayers enables tax authorities to allocate resources, resulting in cost savings more efficiently.
About eight factors are critical for successfully implementing a proactive tax collection strategy based on data analytics. They include data integration and centralization, advanced data analytics and machine learning, a risk scoring system, taxpayer education and outreach, transparency and data protection, a legal framework, capacity building, and continuous monitoring and evaluation. Integrating and centralising data from various sources gives tax authorities a comprehensive view of taxpayer activities. When and where such a system is in place, the IRS must link their databases to them. Examples of the target records include banking data, property registries, business registries, customs records, and the databases of other government agencies. With integrated data in place, the next step is to employ advanced data analytics and machine learning algorithms to identify patterns of non-compliance. Such patterns might include anomalies in financial transactions, inconsistencies in taxpayer lifestyles, or irregularities in business activities.
Developing a risk-scoring system is another critical component of a proactive tax collection strategy. This system ranks taxpayers based on their likelihood of non-compliance. Consequently, it enables tax authorities to identify high-risk taxpayers more efficiently. The system can flag individuals or businesses with a higher probability of non-compliance or fraudulent activity by analysing various data points. Tax authorities can focus their resources on these high-risk individuals, increasing the chances of successful enforcement actions and revenue collection. Secondly, a risk-scoring system enhances the transparency and fairness of tax collection. The system evaluates taxpayers based on objective criteria, removing biases or subjective judgments. By promoting transparency and fairness, tax authorities can build trust with taxpayers, encourage voluntary compliance, and reduce the need for enforcement actions. A risk-scoring system also improves the overall efficiency and effectiveness of tax collection. Automating the risk assessment process saves tax authorities time and resources they would have otherwise spent on manual reviews. This process allows them to reallocate these resources towards activities that have a higher impact on revenue generation, such as conducting audits or providing taxpayer education. Additionally, the system provides valuable insights and analytics that can inform policy decisions and improve tax administration in the long run.
Furthermore, transparency and data protection are paramount in handling taxpayer data. The IRS must treat taxpayer data as sensitive because it contains personal and financial details that citizens entrust to their governments. Therefore, upholding transparency in its usage ensures accountability, builds trust, and safeguards against potential misuse, which is fundamental to effective democratic governance. The first benefit of openness is that it fosters accountability because citizens can scrutinise data on the usage of their tax. This accountability promotes responsible government spending and helps curb corruption. Also, transparency assures taxpayers that they will not use their data for unauthorised purposes, reinforcing trust in the government’s data management practices. Data protection is crucial to safeguarding individuals’ privacy and financial security. Unauthorised access or breaches can result in identity theft, financial fraud, or irreparable harm. Robust data protection measures, such as encryption and strict access controls, are essential to mitigating these risks. However, success in this regard primarily depends on the strength of the legal framework.
A robust legal framework is critical and the cornerstone of proactive analytics-driven tax collection. A sound legal framework mandates that the IRS respect taxpayer rights and privacy while exploiting databases containing their information to enhance revenue collection. It ensures the lawful gathering and analysis of taxpayer data and enforces compliance with relevant laws and regulations. Secondly, it provides a clear legal basis for sharing information with other government agencies at the federal and local levels to combat tax evasion and money laundering where necessary. Overall, a robust legal framework empowers tax authorities at the subnational level to harness the power of data to improve tax collection by ensuring compliance, enabling predictive analytics, and facilitating collaboration while strengthening the tax system’s integrity.
Again, investing in the training and development of subnational tax authorities’ workforce is critical and essential for effectively implementing a proactive tax collection strategy. The IRS personnel need the skills and tools to analyse data, conduct audits, and communicate effectively with taxpayers. Additionally, implementing a proactive tax collection strategy is an ongoing process that requires continuous monitoring and evaluation to ensure its effectiveness. Establishing key performance indicators (KPIs) to measure progress and make adjustments as needed is essential.
Finally, shifting from a reactive to a proactive tax collection strategy in the IRS of states and local governments in Nigeria is essential and achievable with careful planning and execution. Tax authorities at the subnational level can significantly enhance tax compliance by integrating data, leveraging advanced analytics, implementing a risk-scoring system, educating taxpayers, ensuring transparency, developing a supportive legal framework, investing in capacity building, and continuously monitoring and evaluating progress. That should be the new frontier for forward-looking IRS’ across the country.