Mary Nwaife Mezue speaks on Nigeria’s credit gap, informal economy, and the case for Adaptive Credit Intelligence

Nigeria’s credit ecosystem has expanded rapidly in recent years, driven largely by fintech innovation and the rise of digital lending platforms. Yet millions of economically active Nigerians remain outside the formal credit system, unable to access loans despite active participation in the economy.

In this interview, financial systems analyst Mary Nwaife Mezue discusses the structural weaknesses in Nigeria’s credit architecture and proposes what she describes as an Adaptive Credit Intelligence (ACI) model, a framework that could transform how creditworthiness is assessed in emerging economies.

Nigeria has seen rapid growth in digital finance, yet access to formal credit remains limited. What is the core problem?
The uncomfortable truth is that the problem is not that Nigeria lacks creditworthy people. The problem is that the system we use to determine creditworthiness was designed for a very different type of economy.

Traditional credit scoring models evolved in places where most people have salaried jobs, mortgages, credit cards, and long banking histories. Nigeria is not that kind of economy. Here, the informal sector dominates. Millions of traders, artisans, farmers, and small business owners conduct their economic lives outside the formal structures that these models depend on.

So the system looks for signals that most Nigerians simply do not produce. As a result, the majority of economically active adults remain invisible to formal lenders.

You’ve described Nigeria’s credit architecture as having several structural fractures. What are they?

I tend to group them into five.

First is the data desert. A large share of Nigerians do not have credit files with the country’s credit bureaus.
Second is identity fragmentation. Even where behavioural data exists, it can be difficult to link it confidently to individuals across platforms.

Third is the informality paradox. The informal economy generates enormous economic activity, but because it happens outside formal payroll and banking structures, traditional scoring systems cannot read it.

Fourth is regulatory lag. We have regulations governing lenders, but we still lack comprehensive frameworks for evaluating the intelligence systems that determine lending decisions.

Finally, there is the inclusion–protection gap. As lenders try to expand access to credit, they also face rising fraud risks. Many systems struggle to manage both objectives simultaneously.

These five issues are interconnected. Together they reveal a deeper design problem.

You propose an Adaptive Credit Intelligence model. What exactly is that?

The idea is quite simple in principle. Instead of relying primarily on historical financial records, the system reads behaviour over time.

Nigeria already has cultural precedents for this. In traditional savings groups like esusu, isusu, or adashe, members are judged by observed behaviour. Do they contribute regularly? Do they honour commitments? Are they consistent?
An Adaptive Credit Intelligence system would do something similar, but using digital signals generated through everyday economic activity.

For example, it could analyse patterns in mobile wallet transactions, airtime purchases, digital payments, and other behavioural indicators to build a continuously evolving credit profile.

Rather than producing a static score based on limited historical data, the system would adapt as a person’s financial behaviour evolves.

How would such a system work in practical terms?

There are three core capabilities.

The first is continuous behavioural profiling. Instead of waiting for someone to apply for a loan, the system continuously learns from patterns in digital economic activity.

The second is simultaneous fraud detection. Today, lenders often evaluate creditworthiness first and then screen for fraud. An adaptive system would do both at the same time. As the system becomes better at recognising genuine economic behaviour, it also becomes better at identifying suspicious patterns.

The third capability is adaptive learning at scale. The system would learn not just from individual borrowers but from broader economic patterns. Seasonal income cycles, emerging fraud tactics, or shifts in payment behaviour could all be incorporated into its risk models.

Can you give examples of how this might benefit everyday Nigerians?

Imagine a trader in Kano who sells groundnut oil at a local market. She might not have a traditional bank account, but she uses a mobile wallet to pay suppliers and regularly sends money to family members. She recharges her phone frequently and has maintained the same number for years.

Traditional scoring systems see almost nothing. But an adaptive system would see patterns of stability and reliability.
Or consider a shoemaker in Aba with apprentices, regular supplier payments, prepaid electricity, and participation in cooperative savings groups. His income fluctuates seasonally, which traditional models might interpret as risk. But a more intelligent system would recognise it as a predictable seasonal business cycle.

In both cases, credit could be extended in ways that match real economic behaviour.

What role do regulators and policymakers play in making such systems possible?

Regulators have a critical role to play.

Nigeria has already taken some important steps. The Digital Lending Regulations introduced by the Federal Competition and Consumer Protection Commission have begun bringing order to the digital lending space.

But the next step is thinking about credit intelligence infrastructure. That includes standards for transparency, fairness, and auditability in alternative credit scoring models.

Any system that processes behavioural data must also address issues of privacy, consent, and bias. Those safeguards must be designed from the beginning.

Nigeria’s digital economy is growing quickly. Does the country have the infrastructure needed to support this kind of system?

In many ways, yes.

Nigeria has more than 150 million active mobile subscriptions and rapidly growing digital payment adoption. That creates a massive amount of behavioural data that could potentially support alternative credit models.

Of course, infrastructure remains uneven. Connectivity and digital literacy vary across regions. But the trajectory is clearly toward greater digital integration.

The opportunity now is to ensure that the systems we build reflect the realities of Nigeria’s economy.

Finally, what is the bigger question Nigeria needs to answer about its credit system?

The question is not whether Nigerians are creditworthy. Millions of people across the country demonstrate financial discipline every day.

The real question is whether our institutions are willing to build systems capable of recognising that behaviour.
Nigeria does not need a slightly improved version of someone else’s credit scoring model. It needs a system designed for its own economy, one that can read the signals Nigerians already generate and translate them into access to opportunity.