Building for the Wrong Users: Why Nigeria's Fintech Credit Engines Need a Hotfix

If you’ve ever written an if-else statement that looked perfect in local development but completely fell apart when hit with real-world production traffic, you’ll understand how I felt looking at the latest data from Credit Direct.

In our world, we talk a lot about data-driven decisions. We build algorithms, write risk-scoring engines, and integrate APIs to automate who gets money and who doesn't. But the reality on the ground shows that our credit engines are fundamentally misconfigured.

According to Credit Direct’s 2025 Nigeria Credit Landscape Report, which analyzed data from about 300,000 active borrowers, women make up only 26% of those who actually get loans. Men take the lion's share at 74%.

But here is the catch: women are far better at paying back.

Their delinquency rate is a clean 7.8%, while men sit at a much riskier 10.9%. Not only do they pay back better, but they also manage bigger tickets. On average, women borrow ₦478,117 compared to the ₦430,962 that men pull down. Among married folks, the gap widens further—women average roughly ₦500,000 with lower default rates, while married men hover around ₦450,000.

As a developer, if my database queries consistently showed that Node A is 30% more efficient than Node B, but my load balancer kept routing 74% of the traffic to Node B, I’d be up at 3 AM debugging the routing logic.

Our credit distribution is routing traffic to the wrong nodes.

The Legacy Bugs in Our Underwriting Logic

Why is this happening? It’s not because fintech founders are actively trying to ignore women. It’s because the legacy datasets we train our models on are inherently biased.

Most digital lenders in Nigeria still rely heavily on traditional salary accounts, formal employment history, and three-to-six months of bank statements parsed via Mono or Okra. But if you look at the structure of the Nigerian economy—whether you are walking through a market in Onitsha or grabbing lunch near a workspace in Gbagada—you’ll realize that some of the most consistent, cash-flowing micro-businesses are run by women who might not have a neat "Salary" narration hitting their GTBank account every 25th of the month.

They run high-velocity, cash-heavy retail setups. Because our credit scoring APIs are optimized for corporate salary earners (mostly men), we keep writing off the very demographic that actually keeps their word.

We are basically optimizing our apps for the guy who takes a loan to buy a Playstation or show off at a lounge in Lekki, while locking out the woman who needs capital to restock her inventory in Akure and has a track record of paying back down to the last kobo.

Real-world Survival Tech

The second half of the report highlights another hard truth. What are people actually borrowing this money for?

It’s not to scale cloud infrastructure, buy crypto, or fund high-growth tech startups. The top three reasons Nigerians are taking out these loans are rent, medical bills, and school fees.

This isn't "wealth-building leverage." This is survival credit. It’s what keeps families afloat when "Sapa" hits hard and the economy refuses to smile.

When you build products in Nigeria, you quickly realize that your biggest competitor isn't another tech startup; it’s basic survival. If a user has to choose between paying your subscription or paying their kid’s school fees, your app is getting uninstalled immediately.

This is why we need to build credit products that actually map to how Nigerians live. If the primary use case for credit is paying school fees or rent, then maybe we shouldn't just offer generic cash loops. We should be building direct-to-institution payment APIs that offer lower interest rates because the cash never actually hits the user's wallet to be spent on other emergencies.

How Do We Refactor This?

If we want to build sustainable fintech platforms that survive the current macroeconomic squeeze, we have to change our underwriting inputs.

1. Stop relying solely on formal bank statements. We need to plug into alternative data points. Transaction histories from POS terminals, inventory logs from merchant apps, and utility bills can tell a much better story of creditworthiness than a basic bank statement.
2. Design for the informal merchant. If married women are taking larger loans and paying them back more reliably, why aren't we designing specific, zero-friction merchant credit products tailored for them?
3. Build for actual utility. If the data shows that rent and school fees are the main drivers of credit, let's build structural credit partnerships with landlords, schools, and hospitals instead of letting users take expensive, short-term emergency cash loans that push them into debt traps.

We have the tech stack, we have the APIs, and now we have the data. It's time to fix the routing logic. If we don't, we’ll keep funding defaults while the real, creditworthy builders of this economy remain locked outside the gates.