Nigeria’s credit challenge is often misunderstood.
The issue is not that Nigerians are inherently poor borrowers. Rather, the difficulty lies in how creditworthiness has traditionally been assessed. Traditional credit scoring models rely heavily on formal credit histories and bureau data. In Nigeria, however, such data covers only a limited portion of the population, leaving many, particularly low-income earners and participants in the informal economy, without the financial footprint that conventional lenders rely on.
This structural gap is well documented. According to the World Bank and Enhancing Financial Innovation & Access (EFInA), a significant proportion of Nigerian adults remain either unbanked or underbanked, limiting the availability of formal credit data for risk assessment.
At the same time, broader systemic challenges persist. The Central Bank of Nigeria has consistently reported concerns around non-performing loans (NPLs) within the banking sector, although ratios have improved in recent years due to regulatory interventions. In addition, lenders continue to face practical difficulties in debt recovery, including delays associated with dispute resolution and enforcement processes. These factors increase the cost and risk of lending.
Taken together, these issues point to a common underlying problem: the definition and measurement of creditworthiness. When large portions of the population cannot be assessed using traditional tools, access to credit becomes constrained, regardless of actual repayment capacity.
That gap, however, is beginning to narrow. Advances in data analytics and artificial intelligence are enabling lenders to move beyond traditional credit bureau models by incorporating alternative data sources. This shift is gradually expanding the scope of who can be evaluated and how.
This Tech Brief examines that transition and considers what it means for the future of credit in Nigeria.
The Shift: From Credit History to Behaviour
The old model for determining creditworthiness was straightforward: check a person’s credit history, assess their repayment patterns, and make a lending decision. In Nigeria, however, this model has inherent limitations. A large proportion of the population has little or no formal credit history, largely due to low participation in formal financial systems. As a result, lenders are often faced with a difficult choice: decline otherwise viable borrowers or extend credit while assuming higher levels of uncertainty and risk.
Advances in artificial intelligence are beginning to change this dynamic.
The Central Bank of Nigeria, in its 2025 Fintech Report, acknowledged the growing role of AI in reshaping credit risk assessment. The report highlights that AI is already widely deployed across Nigerian fintechs, with about 37.5% of fintechs using AI for credit scoring and risk assessment. This signals regulatory acknowledgement that data-driven, AI-enabled systems are no longer peripheral, but central to the evolution of financial services.
Rather than relying solely on formal credit histories, digital lenders are increasingly using machine learning models to analyse non-traditional data points such as:
- airtime recharge pattern
- utility payment behaviour
- mobile wallet transactions
- digital commerce activity
These models allow lenders to build predictive profiles of repayment behaviour, even in the absence of formal credit records.
For example, digital lenders like FairMoney and Carbon use automated systems to assess applicants and, in many cases, issue credit decisions within minutes. Across the African continent, similar models are delivering results. Platforms like M-KOPA have extended over 440,000 additional credit lines, driven by AI-based repayment predictions.
This shift marks a fundamental change in how creditworthiness is understood. It moves the focus away from what borrowers have done within formal financial systems toward what their everyday financial behaviour reveals about their ability and willingness to repay.
What the Regulatory Environment Looks Like
Regulation is evolving rapidly, and the overall direction is increasingly clear: as the use of AI in financial services expands, so too does regulatory scrutiny.
At the core of Nigeria’s framework is the Nigeria Data Protection Act (NDPA) 2023, which establishes key rights and obligations regarding the processing of personal data. Among its important protections is the right of individuals to object to decisions based solely on automated processing, including AI-driven credit scoring, where such decisions produce significant effects. This mirrors the landmark SCHUFA case (C-634/21) in Europe, which reinforces the principle that automated credit scoring systems must be explainable, subject to safeguards, and open to meaningful human review.
Building on this, the NDPC’s General Application and Implementation Directive (GAID) 2025 now mandates Data Protection Impact Assessments (DPIAs) for high-risk processing. Fintechs deploying AI models without a documented DPIA are already in a state of non-compliance.
Regulatory oversight, however, is no longer limited to data protection alone. A broader ecosystem of rules is emerging to govern digital lending and algorithmic decision-making:
- FCCPC DEON Regulations (2025):Qualified digital lenders must now register with the Federal Competition and Consumer Protection Commission (FCCPC). These rules explicitly target opaque, “black box” lending, where algorithmic decisions are made without any explanation to the borrower, by requiring transparency in how interest rates are set and prohibiting predatory, exploitative, and discriminatory interest rates.
- CBN Open Banking Framework & Payments System Vision 2025 (PSV ) 2025:The Central Bank’s Open Banking framework classifies credit scoring data as “High and Sensitive Risk,” requiring standardised APIs and strict consent management. Furthermore, the CBN 2025 Fintech Report signals a shift toward SupTech (Supervisory Technology), the use of AI and data tools by regulators themselves to monitor and audit fintech operations, where regulators will use their own AI to audit fintech algorithms for bias and stability in real-time.
What Should Lenders and Borrowers Expect?
- For lenders and financial institutions
AI-driven credit scoring has transitioned from a competitive edge to a baseline market expectation. Institutions relying solely on traditional credit bureau checks are increasingly unable to compete with the near-instant decisioning cycles of digital-first lenders. However, as speed increases, so does regulatory scrutiny. Deploying AI without a robust governance infrastructure is now a significant balance-sheet liability.
To navigate this, lenders must prioritize three strategic pillars:
- From “Black Box” to Explainable AI:Global standards are moving toward “white box” models. Fintechs must build explainability into their algorithms from the ground up, utilizing tools that explain how the model reached its decision, like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) which work by breaking down an AI’s decision into its individual components, showing, for instance, how much weight was placed on airtime spend versus mobile wallet activity in arriving at a final credit score. These tools are becoming the industry standard for auditing how specific variables like airtime spend or mobile wallet activity impact a final credit score, ensuring decisions can be justified to both regulators and rejected applicants.
- Data Governance as a Compliance Frontline:With the NDPA and the GAID 2025 in full effect, the use of alternative data requires a rigorous compliance stack. This includes automated Consent Management Frameworks, strict Purpose Limitation policies (ensuring data scraped for scoring isn’t used for unsolicited marketing), and documented DPIAs.
- The High Cost of Non-Compliance:Lenders must remain mindful of the Credit Reporting Act 2017, which clearly mandates written consent before personal data is shared with any third party and safeguards a borrower’s right to confidentiality. The penalties for breaches are severe and may include fines of no less than N10,000,000 or prison terms of up to 10 years. This makes data privacy a matter of personal liability for board-level executives.
- For borrowers
For borrowers, particularly those who have historically been excluded from formal credit systems, AI-driven lending presents new opportunities. Access to credit is expanding as lenders begin to consider alternative indicators of financial behaviour. A person’s digital footprint, such as how they pay utility bills, use their mobile wallet, and manage airtime or data purchases, can increasingly form part of their credit profile. This shift has the potential to bring millions of previously “invisible” individuals into the formal credit ecosystem.
Despite these opportunities, data protection rights remain central. Before a lending platform accesses personal or behavioural data for credit assessment, it must obtain explicit and informed consent. Borrowers should understand what they are agreeing to and how their data will be used. Understanding these rights is essential in a system where financial decisions are increasingly shaped by algorithms.
Where This Is All Heading
The trajectory of AI-driven credit scoring points toward a broader transformation in how financial services are delivered.
Rather than existing as standalone products, credit services are increasingly being integrated into the digital platforms people already use every day, such as ride-hailing applications, e-commerce marketplaces, agricultural supply networks, and mobile payment ecosystems. This model, commonly referred to as embedded finance, allows financial services to be delivered seamlessly within non-financial platforms.
In this context, credit scoring powered by AI is likely to become less visible as a distinct process and more of a background function, operating continuously across digital interactions. Decisions about creditworthiness may be made in real time, based on behavioural data generated through everyday activities.
At a continental level, this shift is occurring alongside rapid growth in the digital and AI economy. Industry projections estimate that Africa’s AI market could reach approximately $16.53 billion by 2030, reflecting increasing adoption across sectors, including financial services. Nigeria, as one of Africa’s largest fintech markets, is well-positioned to play a central role in this growth. However, the extent of that opportunity will depend not only on technological adoption but also on the strength and effectiveness of governance frameworks.
For legal, risk, and compliance teams, this evolving landscape introduces new and ongoing responsibilities. These include:
- ensuring compliance with data protection laws
- conducting and documenting Data Protection Impact Assessments (DPIAs) for high-risk processing
- auditing AI models for bias, accuracy, and explainability
- maintaining robust consent and data governance frameworks
safeguarding consumer rights in automated decision-making systems.
Conclusion
Nigeria’s transition toward AI-driven credit scoring is not merely a technological development. It reflects a broader shift at the intersection of financial inclusion, data governance, and regulatory oversight. The institutions most likely to succeed will not necessarily be those that adopt AI the fastest, but those that deploy it responsibly. This includes building systems that are transparent, explainable, and supported by clear consent mechanisms, effective oversight structures, and respect for the rights of individuals whose data underpins these models.
For professionals operating in this space, staying ahead requires more than an understanding of the technology itself. It requires a working knowledge of the legal, regulatory, and contractual frameworks that define how such technologies can be designed, implemented, and governed.
In this evolving landscape, responsible innovation will be the key differentiator.
Disclaimer: This article is for informational purposes only and does not constitute legal advice.
