Engineering for Scale in African Fintech
Opeyemi Folorunsho, Vice President of Research and Development at Moniepoint, shared valuable perspectives on building infrastructure that supports rapid growth in the African fintech landscape.
From Toy Cities to Financial Platforms
Folorunsho explains his role by imagining a vast toy city where millions interact daily. As its architect, he designs roads, bridges, and traffic systems—not just for today’s needs but with an eye toward future expansion. This approach translates to fintech where Moniepoint must anticipate how infrastructure will handle billions of transactions across multiple regions.
R&D in Action: Beyond the Lab Coat
Moniepoint’s R&D isn’t about theoretical research; it’s applied engineering focused on solving real business challenges. A typical week involves:
- Analyzing complex problems and evaluating technologies
- Building prototypes to test feasibility
- Measuring performance trade-offs
- Determining if an idea warrants production investment
Folorunsho emphasizes that successful R&D reduces uncertainty, enabling informed engineering decisions rather than pursuing research for its own sake.
The Transition from Prototype to Product
For Folorunsho, a prototype graduates to product status when:
- Technical challenges are genuinely solved (beyond a demo)
- The solution simplifies complexity rather than adding it
- It addresses a widespread need across multiple teams
Scaling Infrastructure Across Africa
As Moniepoint expanded, Folorunsho’s focus shifted from “can we make this work?” to “will this survive ten times the traffic without issue?”. This demands:
- Systems handling billions of financial events with low latency
- Robust failure recovery mechanisms
- Consistent performance across diverse network conditions
- Internal platforms that maintain developer productivity during rapid growth
The True Nature of Engineering Research
Folorunsho challenges the misconception that research equals reading papers. Instead, he defines it as:
- Reducing uncertainty through experimentation
- Building prototypes to generate evidence
- Measuring performance and identifying trade-offs
- Being willing to validate or invalidate assumptions
He notes that the most valuable R&D outcome isn’t proving oneself right—it’s learning what won’t work before significant investment.