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Tech Driven Embedded Financing in Africa (1)
Continuing from the last post, after connecting with the previous African microlending company, I happened to come across another firm that also does lending in Africa. The guy running it is named J. He made his early fortune in Silicon Valley investing in startups, and after reaching financial freedom, he turned to philanthropy. But he quickly realized that handing money over to charities wasn’t very effective—so he decided to take matters into his own hands.
He started by helping provide access to clean water in Haiti, but soon saw the massive opportunity in Africa. By serving micro-entrepreneurs in Africa, he found a model that could generate returns for himself, his investors, and the local borrowers. Everything is powered by tech—so it’s profitable and impactful. That’s how he landed on this mission.
Now, the last microloan company we featured in the earlier series followed a very traditional model: lend directly to individual borrowers, collect interest, and repeat. We talked about the risks already—for example, underwriting was basically a black box, and the only data point they used was whether the person paid their phone bill. That’s it. Zero visibility beyond that.
J’s model is entirely different. His company is part of a new wave called embedded financing. Instead of lending directly to end borrowers, they lend to small companies that provide productive assets—in other words, tools that help people make money.
Let me give you an example.
In the last series, we often mentioned the local motorcycle taxi businesses—think of it like Uber, but with motorcycles. It’s a common first step for locals starting their own micro-businesses. Now, there are small companies popping up that supply these motorcycles. But here’s the tech twist: these motorcycles are connected to a backend system, so the company can monitor operational data in real-time—how far each bike travels, how many hours it runs, how many days it’s active, and even the fares passengers pay (yes, just like Uber).
This means the companies know exactly which riders are profitable and which ones aren’t. They can even forecast future performance based on historical patterns. So they know who to lease or sell more bikes to in order to keep the whole value chain profitable. After all, if the rider doesn’t make money, the supplier loses money eventually too.
J’s company lends to these micro-businesses that provide such income-generating tools. They collect these companies’ backend operational data, run their own models, and use that to make lending decisions—in real-time. Profitable micro-businesses get more funding; unprofitable ones get cut off. The entire lending strategy is driven by operational data.
They’ve invested in all sorts of micro-enterprises that provide productive assets—motorcycles, inventory for small-scale import/export businesses, and more. Everything is monitored in real-time. If the data looks good, funding continues. If it doesn’t, it stops. That’s the core of their crazy tech: using operational data to make fast lending decisions and scale quickly.
J says his investors can even access dashboards and loan tapes directly, so he sleeps very well at night 😂
So, where are they lending? What about risk? We’ll cover that in the next post.