Processing $263M in loans: pioneering Nigeria's first digital lending platform
Mid-tier Nigerian commercial bank. 140 branches, ~4,000 employees. Won Central Bank of Nigeria's Innovative Bank of the Year 2019, partly driven by Specta's success. In 2016, getting a loan in Nigeria meant either weeks of paperwork and guarantors at a bank, or hidden fees and exploitation from loan sharks.
I designed and built the frontend for a transparent lending platform that cut processing from weeks to 5 minutes, disbursed $263M in three years, and achieved zero defaults. The counterintuitive finding: explaining the decision slowed nothing down and sped everything up.
A market designed to exploit
In 2016, getting a loan in Nigeria meant one of two things. You went to a bank, filled out stacks of paperwork, found a guarantor, waited 2-4 weeks, and hoped for the best. Or you went to a loan shark who gave you money fast but buried you in hidden fees and unrealistic repayment terms.
Neither option worked for the people who needed credit the most. Small business owners who needed quick inventory funding. Professionals facing unexpected expenses. Anyone without a wealthy guarantor willing to co-sign. Research showed widespread negative lending experiences driven by hidden fees and exploitative conditions. Transparency wasn't a feature request. It was practically nonexistent.
Every bank in Nigeria was sitting on years of customer transaction data: salary deposits, spending patterns, account history, debt coverage ratios. The raw material for automated credit decisions was already there. No one was willing to take the risk. Sterling Bank saw the same data and spotted an opportunity. I was brought in as the sole designer and frontend developer to build Nigeria's first digital lending platform.
Guerrilla research in bank branches
I ran guerrilla research directly in Sterling Bank branches: informal interviews in banking halls, observing how customers interacted with loan officers, listening to the questions they asked and the concerns they raised. I also spent time with credit analysts and branch staff, mapping the manual loan processing workflow from the inside.
Three findings shaped everything.
Documentation burden
The documentation burden was the real barrier, not interest rates or repayment terms. Customers were put off by the process itself: paperwork, guarantors, multiple visits, weeks of uncertainty.
Black box decisions
Customers couldn't see how decisions were made. The approval process was a black box. If rejected, you rarely knew why. This opacity fed distrust because it felt arbitrary, similar to loan shark practices.
Time as real cost
Loan decisions weren't made at branch level. Missing documents sent applications back to the start. For customers who needed credit urgently, every day of waiting was a business cost they couldn't recover.
The barrier to lending wasn't interest rates or terms. It was the process itself: opaque, slow, and built on a foundation of distrust.
The transparency paradox
When I showed customers early prototypes with instant approval, their reaction wasn't excitement. It was suspicion. "How can you approve me that fast? What's the catch?" Years of exploitation had taught Nigerian borrowers that speed meant hidden traps. I had to design a system that earned trust before it earned engagement.
The loan offer screen displays a summary of the factors the system considered: salary consistency, spending patterns, debt coverage, and account history. Not a per-loan algorithmic breakdown, but a transparency layer that replaced the black box with visible reasoning. Slowing down to explain actually increased conversion, because it replaced suspicion with understanding.
Rather than requiring guarantors and paperwork, I designed a system using customers' existing banking transaction data. The platform analyzes spending patterns, income consistency, and debt coverage ratios in real-time. A customer's 'financial footprint' replaces everything that made traditional lending slow and painful. The credit decision happens in under 5 minutes from application to money in the account.
The automated system could pre-approve customers based on their financial capabilities, informing them of the exact range they qualified for before they accepted. Individual risk profiles set the caps. Customers saw their ceiling before committing, not after.
I didn't just design screens and hand them off. I built the production frontend in HTML, CSS, and JavaScript with Bootstrap. The backend team (C# engineers) integrated with my frontend directly. What I designed is what shipped, because I built it. The rapid prototyping cycle, from sketch to coded prototype to stakeholder review to iteration, happened in days rather than weeks.
Two principles guided the design
Both principles came from research findings in a market where trust was the scarcest resource.
In a lending market built on opacity, showing customers exactly what they'd pay mattered more than showing them fast approval. Complete fee breakdowns, real-time interest calculations as users adjusted amounts, and plain-language terms replaced the black box that borrowers had learned to distrust. Explaining the decision actually increased conversion.
Transparency over speedThe bank already held years of transaction data, salary deposits, and spending patterns for every customer. Instead of asking borrowers to produce documents and find guarantors, the platform used what the system already knew to automate credit decisions and pre-populate applications. The automated approach was also more accurate than the manual process, which relied on subjective branch-level advocacy.
System knowledge over customer burdenEarly prototype testing with customers revealed the transparency paradox: instant approval triggered suspicion, not excitement.
This directly led to the credit decision explanation screen.
Beta testing with selected customers in late 2017 validated the core experience before public launch in 2018.
Results that validated our approach
customers served in the first year (2018)
increase in loan applications within the first 6 months
Grew to target of ₦40 billion in disbursements by 2019 (300% increase from year one)
February 2019: Specta opened to all Nigerian bank customers, not just Sterling Bank account holders
Expanded into PayWithSpecta (buy-now-pay-later) during COVID-19 as economic stimulus
Sterling Bank won Central Bank of Nigeria's Innovative Bank of the Year (2019), driven by Specta
“I couldn't believe it was real. In 5 minutes, I had the money in my account. No paperwork, no guarantors, no hidden charges. Specta saved my business when I needed quick inventory funding.”
“After being cheated by loan sharks for years, Specta showed me what honest lending looks like. Everything was clear, and I could even cancel if I changed my mind.”
Trust is the product
In markets with histories of exploitation, earning trust is the prerequisite for everything else. The counterintuitive insight, that explaining slows nothing down and speeds everything up, applies beyond fintech.
Any product entering a low-trust market needs to earn confidence before it can earn engagement.
Broader beta testing. The beta with selected customers validated the core experience, but a larger, more diverse group would've uncovered edge cases in credit assessment earlier, particularly for customers with irregular income patterns common in Nigeria's informal economy.
Earlier merchant partnerships. PayWithSpecta came later as a separate initiative. Designing the merchant credit framework into the original architecture would've made the expansion faster.
When you're the designer and the developer, the feedback loop compresses from weeks to hours. Design decisions get tested in code immediately. Stakeholder reviews happen on working prototypes, not static screens. And what you design is what ships, because you built it.