I’ve been called many things in this space, from pioneer to advocate, and sometimes even crazy. And I wear all of that proudly. Because when it comes to credit, I’m not in this just to build platforms or piggyback on fancy words like “financial inclusion.” What pulled me into credit wasn’t the idea of building software or being recognised for pioneering open banking in Africa. I’ve never been interested in getting points for being early. What mattered then and still matters now is what credit has done and can keep doing for people who would otherwise never get the benefit of the doubt.
I’ve always viewed credit less as a product and more as infrastructure. Something that, when it works well, quietly supports everything else. It makes jobs feel a bit more secure, gives small businesses a chance to breathe, helps kids stay in school and stops people from having to make desperate choices just to get by. Of course, credit doesn’t fix everything, but it does give people room to move. That’s why, even though I spend a lot of time building APIs and platforms and sorting out the technical stuff, that’s never been the point. The real point is what all that effort enables people to do when they’re finally seen by the system.
It’s a bit like designing a tractor. Nobody builds a tractor just to admire its engine or marvel at its mechanics. You build it because you want to solve the problem of hunger. You want to make farming productive enough that people don’t starve. It’s first-principle thinking. Go deep enough, and what you find at the heart of Lendsqr, at the root of Open Banking Nigeria, and even the way I think about APIs, is a single question: how does someone vulnerable get access to data that can prove they deserve a shot at credit?
Because that’s the real problem, everything else is secondary. Until we can find a way to let the system see and understand people who operate on the margins, all the product features in the world won’t mean much. And I’m not just talking about people who are financially excluded by accident. These are people who have been systematically shut out. In countries like Nigeria, Niger, Bangladesh, and Congo, exclusion is structural. It is designed into the way financial systems were originally built. There are no accessible, reliable ways for the poor to prove that they are safe to lend to, even when they are.
Poverty in these places is not a function of laziness or lack of ambition. It is a consequence of being invisible to the institutions that distribute resources. If you don’t have any way to prove your reliability, you won’t get credit. And if you don’t get credit, you’re stuck. You can’t scale a business, handle emergencies, or even take the smallest risks that might improve your life.
That’s why the real work is not just building lending tools, but rebuilding the logic of how we assess people. We can’t keep applying frameworks built for structured, urban populations to people in informal economies. If we don’t expand what we count as data, we’ll keep excluding the exact people credit was meant to help.
Let’s stop pretending trust is enough
Look, I’m Nigerian, and I know firsthand what it means when people say things like, “We believe in our people.” And to be fair, I get where it comes from. It’s a hopeful sentiment. But belief on its own doesn’t protect capital. When you’re the one lending money (money that could make or break your business) you’re not making that decision based on goodwill. You’re looking for something you can point to, something that gives you a reason to believe that the person will pay back. You’re looking for signals that suggest some kind of financial discipline or consistency. And the further down the economic pyramid you go, the harder it becomes to find those signals.
That’s one of the main reasons I advocate so strongly for open banking. Because when it actually works, when all the right structures are in place, it can surface valuable data that no one had access to before. It can show spending patterns, deposits, transfers, bill payments, and all the small behaviours that help lenders build some level of confidence. You can look at that kind of data and say, maybe not with certainty, but with reasonable conviction, that this person has a stable financial routine. And sometimes, that’s all a lender needs to say yes.
Unfortunately, that entire approach depends on one critical thing: that the person you’re evaluating actually has a bank account. And in reality, that’s a big leap. Most people at the bottom of the pyramid don’t have one. They are not part of the formal banking system in any meaningful way. They might be transacting daily, hustling, and moving money around, but none of that shows up in a structured financial footprint.
And even in cases where they do have a bank account, it often doesn’t say much. Maybe they opened it to receive a one-time government transfer or a salary from a temporary job. Maybe they used it a few times and left it dormant. Either way, there’s usually not enough history to make any real judgment about their financial behaviour. The result is that the system continues to cater to people who are already visible, already banked, and already relatively stable. Everyone else is either invisible or misrepresented. So, we end up with an inclusion strategy that excludes the very people it’s supposed to help.
This forces us to confront a tough but necessary question. If banking data isn’t available or isn’t meaningful for a large part of the population, then what do we use instead? What other types of data can give us the kind of visibility we need to make fair lending decisions?
That’s where telecom data starts to look interesting. Not because it’s perfect, but because, in many cases, it’s the only data that exists.
Telecom data is not new, but we haven’t scratched the surface
The idea of using telecom data for credit analysis isn’t something I stumbled into recently. It’s been on my mind for years, not as a theory but as a response to a gap I keep running into. Back in 2021, I co-authored a paper with some good friends of mine titled “Using Open APIs To Drive Financial Inclusion Via Credit Scoring Built on Telecoms Data.” That wasn’t a vanity project or an academic exercise. It came from a real need to find workable alternatives for people who don’t have bank accounts or any traditional financial history. The thinking was simple: if formal financial data doesn’t exist, but almost everyone has a mobile phone, then maybe the phone could tell us something useful.
Back then, the telecom data we had access to was basic. We were looking at call records, airtime recharge behaviour, and SIM registration data. Nothing fancy. But even those simple signals carried meaning. We started asking questions like: Does this person use the same phone number consistently, or are they changing numbers often? Are they topping up their airtime regularly, or is it sporadic? Do they abandon their SIM after defaulting on a loan? Each of these behaviours gave us a small but useful insight into patterns we couldn’t see elsewhere.
Our early work wasn’t technically complex, but that wasn’t the point. We were trying to make sense of what was available and see if we could build basic behavioural profiles out of it. In a context where most people have no credit history, even partial data is valuable. It wasn’t about creating perfect scores; it was about establishing that the data was worth paying attention to. And once you start with something that works, even if it’s simple, you can improve it. That’s how progress happens in spaces where people have been historically excluded. You start with what you have and build from there.
Telecom data today looks very different from what it used to be
Back then, what we had to work with was mostly voice-based information like call durations, contact frequency, airtime recharges, and SIM card activity. That was the standard. It reflected the reality at the time, where mobile usage in many low-income communities was still centered around voice calls and basic SMS. But that version of the world no longer exists. We are now in 2025, and the line between online and offline has blurred, even for the poorest segments of the population. Internet access is no longer a privilege reserved for the “rich”. It has become part of daily life for many people who were previously disconnected.
The people we’re trying to include might not have high-end smartphones or unlimited data plans, but they are online. They are topping up data bundles when they can. They use WhatsApp to stay in touch, they Google things when they’re curious, and they watch YouTube videos, maybe not in high res, and maybe not for long, but they’re there. It might be WhatsApp voice notes instead of phone calls, or YouTube Shorts instead of long videos, but the point is, they are present in the digital world. And that presence leaves a trace.
As a result, the telecom data available today is far more diverse than what we were working with a few years ago. It is no longer limited to who a person calls or how often they top up airtime. It now includes data patterns related to internet usage, geolocation, SIM swapping habits, and device changes. These might seem like insignificant fragments on their own, but together, they can start to paint a picture. A phone that stays in one general area week after week tells a story of stability. Regular data recharges, even in small amounts, suggest some level of income flow. Holding on to the same SIM for a long time can signal accountability, especially in regions where SIMs are easy to discard. These are not perfect indicators, but they are usable. And when traditional credit data is missing, usable data is a good place to start.
To be clear, none of this is about building systems that cross the line into surveillance. It is not about monitoring personal content or tracking browser history. That is not the intention, and it should never be the outcome. What we are talking about is responsibly working with the kinds of data people already produce as part of normal life. These patterns can help open access to credit for those who have been shut out of formal systems, not because they are untrustworthy, but because there has never been a structure in place to recognize the signals they naturally give off. If we can work with those signals thoughtfully and ethically, then we have a chance to finally design credit systems that reflect the world as it is, not just the world as it was imagined decades ago.
There’s a sentience in the data, we just haven’t listened properly
I’m not suggesting that this will be simple. Telecom data, like any data set pulled from real life, will be messy. Some of it will be incomplete. Some of it will be irrelevant. There will be inconsistencies, and there will be noise. But that does not mean it is useless. Buried in that mess is something meaningful. The way people use their phones can tell us more than we think. It’s not about seeing individuals as cold data points. It’s about recognising that these digital patterns reflect real lives. They are expressions of routine, effort, and survival, but we have to be willing to pay close attention to see them.
If we can approach this data with care, if we can build systems that are trained to observe these patterns with respect for context and nuance, then we stand a real chance of unlocking credit for people who have never had access to it before. And when I say credit, I don’t mean a one-time $10 loan through some app. I’m talking about real credit. Credit that can be used to plan, to grow something, to make decisions beyond the next few hours. The kind of credit that opens up options and gives people the freedom to stop living hand to mouth.
That’s what makes this work important. When someone at the very bottom of the ladder gets $100, not as a handout or through pity, but as a loan they are expected to repay with dignity, it can shift the course of their life. That $100 might be used to expand a thriving hustle or send a child back to school.
I’ve seen this play out more times than I can count. I’ve seen people begin with almost nothing and, through access to small or large, well-timed credit, they’ve grown into owners of multiple businesses, employers of labour, and contributors to their communities. I know people who came from poor backgrounds, no connections or safety net, and today they sit at the top of multinational organisations. They lead teams in global banks, tech companies, healthcare, and consulting firms. It didn’t happen because they were lucky. It happened because someone gave them a financial foothold, and they ran with it. That’s not charity. That’s what credit does when it is given with intention and based on a signal that someone bothered to find.
It won’t be easy, but it will be worth it
Telecom data isn’t going to fix everything, and anyone thinking otherwise hasn’t worked in credit long enough. To make it useful, we’ll have to train models on huge amounts of data, from real people, across different geographies and behaviours. That kind of work takes time. It also needs the telecom operators to participate, and that’s a whole challenge on its own. Many of them treat their data like something too valuable to share (understandably so), even when the upside could be huge for the people who rely on their networks every day.
Regulators will also have a role to play. We need rules that protect people from exploitation, but we also need room to experiment. If the laws are too rigid or unclear, nothing will move. And when nothing moves, the people who already have access stay comfortable while the rest remain locked out.
We shouldn’t also pretend it will go smoothly from day one. The first few models will suck, not because people aren’t trying hard enough, but because we’re applying new logic to groups that have never been part of the formal financial system in any real way. There will be false positives and defaults (lots and lots of defaults).
People who should never have qualified will get loans, and people who would have paid back every rand or shilling will be turned away. But that’s part of building a new system from scratch. Risk isn’t something you can eliminate. You learn to manage it better over time. What’s happening now is that entire populations are being shut out, not because they’re too risky, but because we’ve never taken the time to figure out how to measure them properly.
Telecom data gives us a way to start doing that. It’s not flawless, but it’s real. It reflects how people live. It gives us the chance to build something that finally works for people outside the formal system. And that, at the very least, is worth the effort.
There’s real potential here if we can stop waiting for perfect
The people who need this the most are spread across Africa, Latin America, Southeast Asia, rural parts of South Asia, and large regions of the Middle East. It also includes migrant communities living in countries considered developed, where formal systems still overlook or exclude them. These are people who have never had access to a proper bank account, and who have rarely been considered eligible for anything beyond a small airtime loan, if that.
And yet, despite all of that, they’re online. They use phones, even if on prepaid plans. They find ways to stay connected. They generate digital footprints, sometimes in fragments, but still consistent enough to build from. That means there is something to work with, something we’ve ignored for far too long because it didn’t look like traditional financial data.
We don’t have to wait for the perfect infrastructure before we act. We don’t have to keep telling ourselves that once everyone is banked, then we’ll include them. That thinking has kept too many people on the outside, waiting for a system that was never really designed with them in mind. The truth is, we already have the tools. What we need now is the willingness to use them thoughtfully, carefully, but without delay. Because credit, at its core, is not just about lending money or balancing risk. It is about recognising the potential in people who have been excluded for too long, and building systems that finally take them seriously.