Ziggy Ojiegbe

More than 80 percent of employment in Nigeria and several fast-growing African economies is informal. Around 90 percent of consumer spending is conducted in cash. Nearly 60 percent of enterprises are owned by women. Yet an estimated 83 percent of the productive workforce operates outside systems that generate usable economic data.

These figures, drawn from the World Economic Forum article “How technology can help bank Africa’s informal economy,” published in the first quarter of 2026 as part of its Annual Meetings, frame a central paradox in Africa’s economic transformation: the sector that drives the majority of livelihoods remains largely invisible to the data systems now used to measure value, allocate capital, and define participation.

Across the continent, the informal economy employs up to five times more people than the formal sector. It powers consumption, sustains households, and anchors local markets. In many countries, it is not peripheral, it is the economy. Yet because much of this activity is undocumented or only loosely recorded, it rarely feeds into the structured datasets that underpin modern financial systems and policy decisions.

This disconnect is becoming more pronounced as digital technologies reshape how economies function.

Over the past decade, financial institutions have rapidly digitized operations. Artificial intelligence and machine learning now sit at the core of credit scoring, fraud detection, customer engagement, and risk management. Decisions that once depended on human judgement are increasingly automated, driven by models trained on large volumes of historical data.

But those models reflect a narrow slice of economic reality.

They are built primarily on formal data, bank transactions, payroll records, tax filings, and registered business activity. These are the signals that algorithms can easily process, verify, and scale. As a result, the formal economy becomes overrepresented in digital systems, while the informal majority remains largely unaccounted for.

The consequence is a structural bias.

Salaried workers and formally registered businesses generate consistent data trails that fit neatly into algorithmic frameworks. Informal workers do not. Their transactions are often cash-based, their incomes irregular, and their records minimal or non-existent. Even as financial systems become more advanced, they continue to draw intelligence from a limited view of economic activity.

According to the WEF analysis, this creates a situation where the most economically active segment of the population is effectively excluded from systems that determine access to finance and opportunity.

The implications extend beyond financial inclusion. They cut to the core of how wealth is measured.

When informal activity is not captured, productivity is understated. Risk is mispriced. Creditworthiness is misjudged. Small businesses appear less stable than they actually are, and entire sectors are undervalued. This distortion shapes investment flows, policy priorities, and broader economic planning.

In effect, a significant share of Africa’s wealth creation is either undercounted or overlooked entirely.

Yet invisibility in formal datasets does not mean an absence of information.

Informal economies are rich in behavioral data, built through daily interactions and lived experience. Market traders know which customers are reliable because they have observed repayment patterns over time. Transport operators anticipate demand based on routine movement across cities. Savings groups assess trust through consistent contributions and behavior during financial stress. Mobile money agents develop an intuitive understanding of transaction flows that signal stability or vulnerability.

These are structured insights in practice, even if they are not formally recorded.

The challenge, as the WEF article highlights, is that most digital systems are not designed to interpret this kind of data. Instead, they reinforce a narrow definition of economic visibility, one tied to formal documentation rather than lived economic behavior.

As Africa accelerates its digital transformation, this gap risks becoming more entrenched. Systems expected to expand access may instead deepen exclusion, not by design, but by relying on incomplete representations of economic life.

There are, however, signs of a shift.

Financial institutions and fintech platforms are beginning to explore alternative data sources, mobile transactions, micro-payments, trading patterns, savings behavior, and mobility footprints, as more accurate indicators of economic activity. These data points, captured through digital platforms and everyday interactions, are helping to build financial identities for individuals and businesses that previously operated outside formal systems.

In some cases, this is already changing outcomes. Access to microloans is expanding. Credit assessments are becoming more reflective of real-world behavior. Small businesses are gaining tools to manage inventory and stabilize cash flow. Across informal retail networks, the effects are becoming visible in more consistent operations and improved income stability.

Still, progress remains uneven, and the scale of the informal sector means the challenge is far from resolved.

At its core, the issue is not a lack of economic activity, but a lack of representation within the data systems that now shape modern economies. Until Africa’s informal sector is meaningfully captured and integrated into these frameworks, much of the continent’s economic value will remain hidden in plain sight.

In a data-driven world, what is not measured is often treated as if it does not exist. The warning from the World Economic Forum is clear: without more inclusive and representative data systems, Africa risks systematically underestimating the very engine of its growth.