There is a peculiar paradox at the heart of Nigerian lending. On the one hand, demand for credit has never been higher. The current credit gap in Nigeria is approximately ₦24 trillion, a staggering shortfall driven by low incomes, rising living costs, and a population that genuinely needs access to finance.
On the other hand, millions of Nigerians who want credit and can likely repay it keep getting turned away, not because they are bad borrowers, but because the traditional system has no record of them.
These are your thin-file applicants, and if you are running a lending business in Nigeria today, they are your biggest challenge and your biggest opportunity.
Let's explore how.
Who Exactly Is a Thin-File Applicant?
A thin-file borrower is someone with little to no formal credit history, either because they have never taken a loan from a bureau-reporting institution or because their financial life exists almost entirely outside the formal system. In Nigeria, this is not a niche edge case, but the majority.
CRC Credit Bureau, Nigeria's largest credit bureau, holds the credit scores of just 33 million Nigerians, out of a population of over 233 million. That means roughly 86% of the country has no formal credit record at all. Meanwhile, 26% of Nigerian adults, approximately 28.8 million people, remain completely excluded from the financial system altogether.
The structural reason is straightforward: the informal sector accounts for about 90% of all new jobs, 60% of urban jobs, and 50% of Nigeria's GDP. When the overwhelming majority of economic activity happens off the books and outside formal institutions, traditional credit scoring fails almost by design.
In 2024, 92.7% of Nigeria's labour force was engaged in informal employment, market traders, artisans, gig workers, and small business owners, most of whom earn real money but have no payslips, no tax filings, and no credit trail to show a lender.
The question, then, is not whether to serve this segment. It is how to assess them accurately.
Here are ten ways to do it.
1. Open Banking Data: The Most Reliable Window Into Real Financial Behaviour
If a thin-file applicant has a bank account, even one they use infrequently, their transaction history tells you more than any credit score ever could.
Nigeria's open banking ecosystem officially went live in August 2025, enabling customer-authorized data sharing via standardized APIs across all regulated banks and fintechs. This is a genuine inflection point for credit assessment in Nigeria.
With customer consent, you can now pull verified account data, inflows, outflows, recurring payments, and savings behaviour directly from a borrower's bank. You can see whether they earn consistently, how they manage their obligations, and whether they have a history of maintaining positive balances. This moves credit decisioning from guesswork to evidence.
Platforms like Zeeh, with features like Connect, are built precisely for this use case: accessing verified transaction data through open banking infrastructure to build a real financial profile on applicants who would otherwise look invisible on paper.
2. Cash Flow Analysis: Measure Income Consistency, Not Just Income
For informal earners, what matters is not how much someone earns in a single month; it is whether their income is consistent and whether they manage it well. For example, a market trader who earns ₦150,000 every month for twelve straight months is a fundamentally different risk from someone who earned ₦400,000 once.
Cash flow analysis examines the pattern of cash movement over time: the regularity of inflows, the average balance maintained, the seasonality of income, and the frequency of withdrawals.
These patterns are more predictive of repayment ability than a point-in-time income figure. According to the Bank for International Settlements (BIS) 2024 report, integrating cash-flow analytics enhances the accuracy of credit risk models and expands access to finance for underserved consumers.
3. BVN-Linked Data: Nigeria's Unique Identity Anchor
Nigeria's Bank Verification Number (BVN) is one of the most underutilised tools in credit assessment. Introduced in 2014, it links a borrower's biometric identity to all their registered bank accounts across every financial institution in the country.
This is powerful, meaning that even if someone has never borrowed formally, a BVN lookup can reveal their banking relationships, account standing, and, in some cases, cross-bank activity patterns.
For thin-file borrowers, BVN lookups also help accurately confirm identity, reduce fraud risk, and surface existing financial relationships that don't appear in credit bureau searches. Lenders who integrate BVN checks into their onboarding and risk assessment processes have a meaningful advantage over those who don't.
4. Telco Data
Nigeria has one of the highest mobile penetration rates on the continent. There are about 222 million mobile subscribers in Nigeria, a data source that can provide information on nearly every resident in the country.
Telco data as a credit signal covers airtime usage consistency, mobile money activity, top-up frequency, data subscription behaviour, and bill payment history. A borrower who consistently pays their airtime and data bills on time, even in small amounts, is demonstrating financial discipline.
That pattern is a legitimate proxy for creditworthiness when traditional bureau data is absent. Telco-linked credit signals are increasingly part of the alternative data toolkit for Nigerian lenders, and they work particularly well for applicants in rural areas where banking penetration is still low.
5. Utility and Rent Payment History: Overlooked but Highly Predictive
Every month, millions of Nigerians pay rent, electricity bills, and other recurring obligations on time, without any of it ever being reported to a credit bureau. These payments are among the most disciplined financial commitments people make, and their consistent fulfillment is a strong indicator of someone's willingness and ability to honour obligations.
Transactional data, such as utility bill payments and telco top-up payment histories, are now recognised as alternative data for assessing real-time financial behaviour and building dynamic borrower profiles.
For Nigerian lenders building scoring models, incorporating rent and utility payment verification, even informally gathered and confirmed, can meaningfully improve prediction accuracy on thin-file borrowers.
6. Digital Footprint Analysis: Behaviour Signals at Scale
Nigeria had more than 97 million internet users by the end of 2024, with 43 million active on social media and 2.6 million using subscription-based video-on-demand (SVoD) services. Each of these represents a signal.
Digital footprint analysis examines a borrower's online presence, app usage, device metadata, and subscription patterns to build a behavioural credit profile.
Someone with a stable email address used consistently over the years, active professional profiles, and subscription services they maintain and pay for is revealing something about their financial stability, even if a credit bureau knows nothing about them.
This is not about surveillance. It is about using consented, available information to extend a more complete picture of who a borrower is. The key is to build scoring models that appropriately weigh these signals relative to traditional factors.
7. Psychometric and Behavioural Scoring: Assessing Repayment Intent
Willingness to repay is just as important as the ability to repay. Psychometric credit scoring uses structured assessments, questionnaires, decision-making scenarios, and value-based questions to evaluate a borrower's attitudes toward debt, financial responsibility, and honesty.
This method has been validated in multiple emerging market contexts and is particularly valuable where financial data is sparse.
It helps lenders distinguish between borrowers who are thin-file because the system never captured them and those who may have actively avoided formal financial reporting. Combined with other data signals, psychometric tools add a layer of human insight to automated risk decisions.
8. Social Vouching and Guarantor Networks: Trust as Collateral
Nigeria has a deep culture of community-based financial accountability, esusu, ajo, and cooperative societies, where trust and social accountability already function as an informal credit infrastructure.
Formalising this through social vouching or guarantor-linked loan structures provides lenders with a meaningful risk-mitigation tool for thin-file borrowers. Moniepoint's informal economy report confirmed that informal business owners rely heavily on personal networks for financing, indicating a significant gap in formal financial services for this segment.
Rather than fighting this reality, smart lenders are building it into their credit models, using verifiable social relationships, community membership, and guarantor willingness as signals of borrower seriousness and network-backed accountability.
9. Business Performance Data for MSMEs: Revenue Over History
For business borrowers who represent a large share of Nigeria's thin-file applicants, the right question is not "what is your credit score?" but "what does your business actually generate?"
Nigeria's informal economy accounts for approximately 58% of GDP, and the owners of these businesses often have robust, verifiable revenue streams despite lacking formal financial records.
POS transaction data, sales records, inventory turnover, supplier payment histories, and even market stall occupancy duration can be used to meaningfully assess MSME creditworthiness. Nigeria's formal lending penetration sits below 5%, yet the appetite and underlying repayment capacity are there; the challenge is building assessment frameworks that can capture them.
10. Thin-File Scoring Models: AI and Machine Learning Built for the Nigerian Context
All the alternative data signals above are most powerful when they are fed into machine learning models specifically trained on Nigerian borrower behaviour. Generic global credit models carry significant bias when applied to markets like Nigeria; they were calibrated on formal, salaried, credit-bureau-tracked populations.
The AFI 2025 report on alternative data for credit scoring confirms that digital lenders in emerging markets are adopting these data sources to responsibly expand credit access while maintaining sound portfolio performance.
Building or adopting models trained on local data, with variables weighted for Nigerian income patterns, repayment behaviour, and informal market dynamics, produces significantly more accurate risk assessments than any off-the-shelf solution.
This is where the real competitive advantage lies for Nigerian lenders willing to invest. It is not in stricter gatekeeping, but in better-calibrated intelligence.
The Cost of Getting This Wrong
Over-approving thin-file borrowers without proper assessment leads to defaults, but over-declining them has its own very real cost.
The amount of credit disbursed in Nigeria rose from ₦2.41 trillion in January 2023 to ₦3.82 trillion in January 2024, yet the credit gap continues to widen because assessment methods are not keeping pace with demand.
Every creditworthy borrower your institution turns away because your scoring model doesn't know what to do with them is revenue lost, portfolio depth forfeited, and market share ceded to the lenders willing to build smarter.
Where Zeeh Fits In
Zeeh’s Connect and Insights are built for exactly this challenge. Using open banking infrastructure and bank transaction data, accessed with customer consent, Zeeh allows lenders to see verified income consistency, spending behaviour, and financial patterns for borrowers who would otherwise appear thin-file.
Paired with ZeehID for identity verification, you can onboard and assess Nigerian borrowers who have never held a formal credit product with confidence, compliance, and speed.
The thin-file problem in Nigeria is not a permanent condition. It is a data gap, and the right infrastructure closes it. Explore Zeeh’s product suites now or talk to our sales team to get started.
