Nigeria’s financial landscape is vast and complex, supporting millions of accounts and fueling businesses across the country. Yet despite its scale, the banking sector continues to grapple with loan defaults, a persistent issue that reduces profitability, limits credit access, and increases risk for both lenders and borrowers.
Traditional credit evaluation methods — relying on limited demographic data or incomplete credit bureau reports — struggle to keep pace with Nigeria’s rapidly evolving economy. Many borrowers operate outside formal financial channels, leaving banks without a clear picture of repayment ability. As a result, strong candidates are sometimes denied loans, while higher-risk borrowers slip through.
AI: Transforming Risk Assessment
Artificial intelligence (AI) is emerging as a solution capable of bridging this gap. By analyzing large, diverse datasets, AI can detect subtle patterns that traditional models miss, enabling lenders to more accurately predict defaults, reduce losses, and extend credit responsibly.
One standout example comes from Emmanuel Adefila, an AI specialist with an MSc from the University of Bradford. Drawing on peer-to-peer lending data, he developed a cloud-based system capable of predicting loan defaults using techniques like Random Forests, Gradient Boosting, and Decision Trees. Ensemble models achieved over 88% accuracy, identifying key risk factors such as income-to-loan ratios, repayment history, and debt-to-income levels.
Deployed as a Flask API on Heroku, Adefila’s system allows real-time access for digital lending platforms without requiring costly infrastructure — a model Nigerian banks and fintechs can readily adopt.
Leveraging Local Data for Smarter Lending
Banks in Nigeria already collect a wealth of data, from BVN-linked account histories and mobile transactions to POS records and utility bill payments. Integrating these data sources into AI models allows lenders to evaluate applicants dynamically, considering:
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Consistency in bill payments
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Cash flow patterns from business POS terminals
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Savings and withdrawal behavior across accounts
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Mobile money transfer trends
Such insights enable more precise credit scoring, safer lending, and broader financial inclusion, especially for individuals and SMEs previously excluded from formal credit systems.
Lagos: The Hub of AI-Driven Fintech
Lagos-based fintechs like Carbon, Renmoney, and FairMoney are already experimenting with AI-based credit scoring, creating a testing ground for innovative solutions. Fintech agility, combined with banks’ capital and regulatory expertise, provides the perfect ecosystem for AI to optimize lending while managing risk effectively.
Overcoming Challenges
Despite its promise, AI adoption faces obstacles:
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Data fragmentation across institutions
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Regulatory compliance under CBN guidelines
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Infrastructure and cybersecurity needs
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Shortage of skilled AI professionals
Pilot programs targeting select loan products can help banks and fintechs gradually scale adoption while mitigating risks.
Broader Economic Implications
Effective AI integration could reshape Nigeria’s credit environment:
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Lower default rates and healthier bank balance sheets
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Increased credit access for individuals and small businesses
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Growth and job creation for SMEs
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More resilient financial systems for regulators
By making loans safer and more accessible, AI has the potential to drive entrepreneurship, innovation, and financial inclusion nationwide.
The Road Ahead
Projects like Adefila’s demonstrate that AI-driven lending is not just theoretical — it’s practical, scalable, and impactful. Nigeria is uniquely positioned to lead Africa in AI-powered financial innovation, building a lending ecosystem that benefits both lenders and borrowers while driving economic growth.
