By Juliet Umeh

Credit scoring models powered by alternative data have become commonplace across Nigeria’s financial sector. What was once considered experimental, using telco metadata, geolocation patterns, and behavioral spending insights, has now become the industry benchmark. But few remember that the model everyone is racing to adopt today started with a radical pivot led by Olumuyiwa Orukotan.

Back when it started, Olumuyiwa introduced a fundamentally different approach to credit intelligence. He challenged traditional assumptions by designing behavioral AI systems tailored for the informal economy, systems that didn’t just assess risk but contextualized it. His model fused regional transaction behaviors with mobile device patterns to generate highly adaptive credit profiles that aligned with the realities of unbanked and underbanked populations.

The result wasn’t just improved lending. It was a high drop in default rates in high-risk zones, combined with a surge in digital onboarding across non-urban clusters. Those outcomes caught the attention of both banks and fintech founders, sparking a wave of copycat systems. Within a year, startups across Lagos, Kaduna, Aba, and Port Harcourt began citing Orukotan’s framework as the technical blueprint behind their own AI-driven credit tools.

This year, what was once considered advanced is now entry-level. And that shift is largely attributed to how Olumuyiwa open-sourced portions of his architecture, mentored technical leads at emerging credit platforms, and presented early validation metrics at regional fintech consortiums. His emphasis on explainability and fairness, especially when modeling for diverse customer segments, further cemented trust in the methodology.

The widespread adoption wasn’t accidental. His frameworks were built for replication. They were modular, interpretable, and designed to be stress-tested across different telecom and banking infrastructures. As of this year, at least four national credit registries and over a dozen lending firms rely on derivatives of his core model to make lending decisions daily.

Olumuyiwa’s success lies not in how loudly he promoted his work, but in how well it worked. He didn’t just build a smarter credit engine; he recalibrated the rules of trust and risk in a system historically designed to exclude.

In doing so, he has quietly positioned himself as the architect behind one of the most important evolutions in African consumer finance.