For decades, one of the biggest obstacles to economic growth in Nigeria has been the credit gap. Millions of individuals and small businesses operate without access to affordable loans, not because they lack potential, but because traditional credit systems see them as “unbankable.”
Banks and lenders have long relied on rigid credit scoring models — often based on limited financial histories, formal employment records, and collateral requirements. In a country where large sections of the population work in the informal sector, those criteria exclude far too many.
Enter AI-driven credit risk modelling, a technology that promises to change the game. By leveraging artificial intelligence to analyse a much broader range of data, lenders can assess risk more accurately, reduce defaults, and most importantly, open the door to borrowers previously shut out of the financial system.
Why traditional models fall short
Conventional credit scoring relies heavily on structured data — loan repayment histories, bank account activity, and documented income. In developed markets, that works because most people operate within formal financial systems.
But in Nigeria, the reality is different:
- Low credit bureau coverage – Many potential borrowers have no formal credit history.
- Informal income streams – A street vendor or ride-hailing driver may earn steadily but lacks payslips or audited accounts.
- Collateral challenges – Many SMEs can’t provide the kind of security traditional lenders require.
The result? A significant chunk of the population is excluded from formal lending, leaving them reliant on expensive informal credit sources.
AI-driven credit risk models take a far more flexible and data-rich approach. Instead of relying solely on traditional financial data, they can incorporate alternative data sources such as:
- Mobile phone usage patterns – Call frequency, airtime top-ups, and payment history with telcos.
- Utility bill payments – Consistent electricity or internet bill payments can indicate reliability.
- E-commerce activity – Transaction patterns on platforms like Jumia or Konga.
- Social media behaviour – Network stability and professional profiles on LinkedIn or other platforms.
- Geospatial data – Business location and activity in relation to local economic hubs.
By processing these diverse data points, AI models can build a far more nuanced risk profile, enabling lenders to serve customers who were invisible to traditional systems.
Imagine a microfinance institution in Lagos evaluating a loan application from a small tailoring business. The owner has no bank statements and no registered business records. A traditional credit model might automatically reject the application.
An AI-driven system, however, could pull in alternative indicators:
- Mobile money transaction history showing steady income flows.
- Regular monthly electricity payments.
- Positive customer reviews on an online marketplace.
- Geolocation data showing consistent operation in a busy commercial district.
Together, these signals might produce a strong creditworthiness score — enough for the lender to approve the loan with confidence.
Benefits for lenders and borrowers
The potential impact is huge:
- Reduced default rates – More data points mean better predictions and fewer risky loans slipping through.
- Expanded lending base – Lenders can profitably serve segments previously considered too risky.
- Faster approvals – Automated analysis reduces manual underwriting time from days to minutes.
- Fairer assessments – Borrowers aren’t penalised for lacking traditional documentation if other indicators show reliability.
For borrowers, this means not just access to capital, but potentially lower interest rates, since risk is assessed more precisely.
Like any technology, AI credit modelling comes with caveats:
- Data privacy – Using alternative data must comply with Nigeria’s data protection regulations and maintain customer trust.
- Bias in algorithms – If historical lending patterns were biased, AI trained on that data could perpetuate inequality unless carefully monitored.
- Infrastructure gaps – Reliable data collection requires robust digital infrastructure, especially in rural areas.
- Regulatory alignment – The Central Bank of Nigeria will need to provide guidance to ensure AI-driven models meet compliance standards.
Addressing these issues will be critical to building sustainable adoption.
Nigeria is uniquely positioned to benefit from AI-driven credit risk modelling. Mobile penetration is high, fintech adoption is growing rapidly, and there’s a vibrant ecosystem of startups already experimenting with alternative data lending.
Companies like Carbon, FairMoney, and Branch are using machine learning to make instant lending decisions, while some traditional banks are beginning to integrate similar capabilities into their risk departments.
If regulators, lenders, and tech companies collaborate effectively, AI-powered credit scoring could help unlock billions in lending for Nigeria’s SMEs and low-income households — fuelling entrepreneurship, job creation, and broader economic inclusion.
Bottom line: AI won’t erase credit risk, but it can make that risk measurable for a far wider range of borrowers. And in a country where financial exclusion remains one of the biggest barriers to growth, that shift could be transformative.
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