If Your Credit Algorithm Ignores Women, Your Code is Broken

If I pushed a pull request where the routing logic sent 74% of user traffic to a server that has an 11% failure rate, while leaving a faster, 7.8% failure-rate server at only 26% capacity, my team would probably revoke my GitHub access before lunch.

Yet, this is exactly how consumer credit is currently running in Nigeria.

Credit Direct’s 2025 Nigeria Credit Landscape Report just dropped, and the numbers are glaring. Women represent only 26% of active borrowers, but they outperform men across every major repayment metric. Their delinquency rate is 7.8%, compared to the 10.9% we see with male borrowers. They even borrow larger average amounts (₦478,117 vs ₦430,962). For married women, the average jump goes up to roughly ₦500,000, all while keeping defaults low.

As someone who spends his days looking at system architectures and user flows, this looks like a massive system bug. We are building fintech products that consistently ignore our most reliable user base.

Why Our Underwriting Models are Failing

When we sit in Gbagada workstations or coffee shops in Abuja writing Python scripts to parse bank statements, we tend to build for the "ideal" user profile we see in textbooks. We plug in APIs from Mono or Okra, scrape three months of transaction history, and check for standard corporate payroll markers.

But this approach is fundamentally lazy.

In Nigeria, a massive chunk of economic activity happens in the informal sector. Go to a bustling market in Onitsha or a beauty salon in Akure. The female business owners there are managing cash flows that would make your favorite SaaS startup look like a joke. They manage risk because they have to—there is no venture capital cushion. For them, defaulting isn't just a credit score drop; it’s a direct threat to their household's daily bread.

Yet, because our risk engines are optimized for clean, formal PDF payslips, these high-performing borrowers get locked out or underserved. We are optimization-blind.

The "Sapa" Stack: What People Are Actually Borrowing For

The other sobering part of the Credit Direct report is what Nigerians are actually using this money for.

It is not to build the next big app or buy prime real estate. The top three drivers for borrowing in the report are rent, medical bills, and school fees.

This isn't investment capital; it’s survival capital. People are literally patching up holes in their basic living standards because inflation is eating their savings alive.

When you realize your users are borrowing just to keep their kids in school or avoid getting evicted by a Lagos landlord, the UX requirements of your app change. Your onboarding shouldn't feel like a cold corporate interrogation. It needs to be fast, clear, and incredibly transparent about interest rates and repayment schedules.

Refactoring the Fintech Playbook

So how do we, as developers and founders, fix this mismatch?

First, we need to stop relying solely on traditional credit bureau data that favors formal sector employees—a demographic that skew heavily male in Nigeria’s formal corporate landscape.

We need to build better alternative data pipelines. Let’s start looking at utility bill consistency, inventory purchases from wholesale distributors, or peer-to-peer transaction volumes on mobile money platforms.

Second, we need to build flexible financial products. If a woman running a wholesale store in Owerri has a business cycle that peaks every two weeks, why are we forcing her into a rigid weekly or monthly repayment schedule? Our databases can handle dynamic repayment rules; we just need to write the logic for it.

The data is telling us exactly where the low-risk, high-value borrowers are. We can keep building shiny landing pages and chasing the same male salary earners who default at 10.9%, or we can refactor our systems to serve the women who actually keep the economy moving.

It’s time to update our code.