There is a story we tell about tax compliance in Africa.
It goes like this: people do not pay because they do not want to. They distrust governments. They operate in cash to stay invisible. They pass wealth through informal channels because the formal system cannot touch them. The problem, in this telling, is fundamentally behavioural, a population that has opted out of the social contract.
I have spent years inside financial services and compliance systems, first in Nigeria, now in the UK. And I believe that story, while not entirely wrong, is mostly a distraction.
The deeper problem is not behaviour. It is data.
What we mean when we say compliance failure
When a tax authority identifies a compliance gap, a business that has under-reported, an individual whose declared income does not match their lifestyle, or a sector where filings consistently understate activity, the instinct is to reach for enforcement. Audit more. Fine, more. Make the penalties severe enough to change behaviour.
But enforcement only works when you can see who you are enforcing against.
And in much of Africa, the tax system is trying to enforce rules against people and businesses it cannot fully see. Not because those people are hiding, though some are, but because the data infrastructure that would make them visible does not yet exist.
Consider what a functional fiscal data system requires: consistent business registration, digital financial records, cross-institutional data sharing between banks and revenue authorities, and a reliable identifier that connects an individual or entity across different systems. In the UK, much of this is in place. In Nigeria, it is under construction. In many African economies, it is still on the drawing board.
You cannot close a gap you cannot measure. And you cannot measure a gap that your data infrastructure cannot see.
The informal economy is a data problem, not a culture problem
The most common explanation for Africa’s tax gap is the size of the informal economy. Across sub-Saharan Africa, informal economic activity accounts for an estimated 30 to 40 per cent of GDP. The argument goes: informal actors do not pay tax because they are outside the system.
But why are they outside the system?
In many cases, it is not by choice. It is because the system was not designed to include them. The registration processes are cumbersome. The filing requirements assume a level of digital literacy and infrastructure that many small businesses do not yet have. The cost of compliance, in time, in fees, and in complexity, is simply higher than the cost of staying informal.
This is a design problem. And design problems are solved with better data, not stricter enforcement.
When you understand, at a granular level, who is in the informal economy, what they earn, how they transact, and what barriers prevent them from formalising, you can design systems that actually bring them in. Rwanda’s revenue authority has done this with some success, using data-driven approaches to identify informal traders and create simplified compliance pathways. The result has been a measurable increase in the tax base, not just in enforcement rates.
The enforcement trap
There is a pattern I have observed across different compliance contexts: when data is weak, authorities default to enforcement. And when enforcement becomes the primary tool, it creates a cycle that makes data weaker.
Businesses that fear arbitrary audits have an incentive to keep records minimal and transactions opaque. Individuals who distrust the authority’s use of their financial data have an incentive to keep that data out of the system. The very conditions that make enforcement necessary also make the data that enforcement depends on harder to obtain.
The only way out of this trap is to invest in the data infrastructure that makes enforcement less necessary, systems that are visible, consistent, and fair enough that the cost of compliance falls below the cost of evasion.
This is what the UK has been building, imperfectly and incrementally, for decades. It is what Africa’s most successful revenue authorities, Rwanda, South Africa, and Kenya, are beginning to build now.
What data-driven compliance actually looks like
I want to be specific, because this conversation often stays at a level of abstraction that makes it feel distant from practice.
Data-driven tax compliance means, at a minimum, four things.
First, it means consistent digital identification, a system where every individual and business has a unique fiscal identifier that follows them across interactions with banks, employers, suppliers, and government agencies. Nigeria’s Tax Identification Number system is a step in this direction, but its coverage and integration remain incomplete.
Second, it means interoperability between institutions. Tax authorities need access to financial data from banks, from employers, and from customs agencies. In the UK, this happens through mandated reporting frameworks. In Africa, it requires both the legislative mandate and the technical infrastructure to make that data flow consistently and securely.
Third, it means analytical capacity within revenue authorities. The data is only useful if there are people and systems capable of reading it, identifying anomalies, building compliance models, and distinguishing between genuine non-compliance and reporting errors. This is where investment in data skills inside public institutions becomes critical.
Fourth, it means feedback loops. Taxpayers need to see that the data they provide is being used fairly, that the system is not simply building a surveillance apparatus but genuinely improving the accuracy and equity of tax administration. Trust, in this context, is not a soft goal. It is a data quality problem. People who distrust the system provide lower-quality data. Better data produces better outcomes, which builds trust, which improves data quality further.
These are not abstract principles. They are specific, buildable things. The question is whether African governments, revenue authorities, and the professionals who serve them are ready to treat them as priorities.
The role of practitioners like me
I am writing this not as a policy analyst or an academic but as a practitioner, someone who has worked inside financial compliance systems in Nigeria and the UK and who is actively building the skills to work at this intersection professionally.
I believe that the professionals closest to these systems have a role that goes beyond implementation. We have the most accurate picture of where the gaps are, where the data breaks down, and what solutions are plausible rather than merely theoretical.
That knowledge needs to be part of the conversation about how Africa’s fiscal systems develop. Not just at the level of policy papers and conferences, but in the practical, daily work of building systems that see more clearly, report more accurately, and enforce more fairly.
Africa does not have a tax compliance problem. It has a data problem, and data problems are solvable.
