By Temilade Alonge
President Bola Ahmed Tinubu
For decades, Nigeria worried about the unbanked, the undocumented, and the invisible. Today, that problem has been solved. The poor are no longer invisible. They are registered, profiled, scored, and tracked. And finally—included.
Across Nigeria, digital finance, aid programs, and identity systems have pulled millions into formal structures. Bank accounts are opened in minutes. Loans arrive in seconds. Aid is distributed through phones and biometrics.
From Abuja to rural communities, technology promises dignity, access, and efficiency. But there is an uncomfortable question we avoid asking: visible to whom, and at what cost?
Nigeria’s digital lending sector has expanded rapidly, driven by platforms such as FairMoney, PalmCredit, Branch, and Carbon. These services promise quick credit to individuals excluded from traditional banking systems. Yet regulatory investigations show that many digital lenders rely on extensive data harvesting, including behavioral and device-level data, to automate credit decisions (FCCPC, 2022).
For borrowers facing urgent needs—food, rent, school fees, medical bills—consent becomes procedural rather than meaningful. Scholars describe this dynamic as coercive consent, where economic vulnerability undermines the ethical basis of data agreement (Barocas & Nissenbaum, 2014). Poverty itself becomes collateral.
The same data logic increasingly shapes humanitarian and development interventions in Nigeria. Social investment programs and NGO-led cash transfers rely on biometric registration, digital identity verification, and mobile money platforms to improve efficiency and accountability (World Bank, 2021).
While effective, these systems also create long-term data trails for beneficiaries, often without clear limits on retention or reuse.
Artificial intelligence systems depend on large volumes of structured data and perform best where behavior is predictable.
Poverty produces constrained and repetitive decision-making patterns, making low-income populations ideal for algorithmic modeling (Eubanks, 2018). In Nigeria, AI-driven systems increasingly influence credit scoring, fraud detection, and aid targeting.
Automation is often presented as neutral, but research shows algorithmic systems frequently reproduce existing inequalities when trained on biased data (O’Neil, 2016). Errors are difficult to challenge, and transparency is rare.
As Nigeria approaches election cycles, the political implications become sharper. Data generated through fintech platforms, telecom services, digital ID systems, and aid programs enables precise mapping of economic vulnerability. This allows targeted political messaging aimed at populations least able to resist manipulation (Kreiss, 2016).
This does not require conspiracy—only aligned incentives: political actors seeking precision, platforms seeking profit, and regulators struggling to keep pace.
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Nigeria is quietly building a society where the poor are the most documented citizens, while the powerful remain the least scrutinised. Market women must surrender biometrics to receive aid. Students must trade phone data for emergency loans. Informal workers are scored, flagged, and profiled by systems they do not understand and cannot challenge.
This is not inclusion. It is exposure.
Poverty in Nigeria no longer only limits income; it increases extractability.
The less you have, the more of yourself you must surrender—your data, your behaviour, your digital shadow—just to survive.
If this trajectory continues, the country risks creating a digital underclass: citizens visible enough to be monitored, but never powerful enough to be protected. Innovation without safeguards does not reduce inequality; it simply modernises it.
True inclusion will not be measured by how many people are captured into systems, but by how many can refuse unfair terms, question automated decisions, and demand accountability.
Visibility without power is not empowerment. It is vulnerability.
