Adedeji Olowe

Credit in developing countries has always been a struggle, and it’s not that lenders in these regions don’t know how to lend. In fact, some of the most resourceful lending innovations I’ve seen have come out of Bangladesh, Nigeria, Kenya, India, and Peru. But the problem is that the entire credit environment is stacked against good lending outcomes. 

In most of the markets I’ve worked in, whether in sub-Saharan Africa, across large parts of Latin America, through South Asia, or even in the Middle East, there are structural weaknesses that make lending incredibly risky. The rule of law is often shaky or slow to respond. Data systems are fragmented or simply unavailable. And there is usually no consistent legal or reputational consequence when borrowers default. 

The idea that a borrower will repay because they’re afraid of what will happen if they don’t simply does not hold. You can miss five loans with five different lenders and still walk into a sixth loan application with no record of the past five. Blacklists exist, but they rarely work the way they should. Courts may technically be an option, but they are expensive, time-consuming, and offer no guarantees. In most cases, lenders are left to eat the loss.

Because of this, we’ve had to rely on what I often call the “spirit of the Lord” to make lending decisions. While I would like that to be some silly joke I shipped in to lighten the mood, it is not. We lean on gut feeling, pattern recognition, and learned instincts. You try to make sense of behaviors and signals that may not be written down anywhere. You might use airtime top-ups, device type, social circles, or informal savings patterns. None of this exists in a formal credit policy document, but it becomes the basis for whether you disburse or not. There is no clean formula, and there is rarely any objective way to explain your decisions. You just go with what your experience tells you feels right.

Now, AI shows up with the promise of order, structure, and prediction. For the first time, it looks like we might be able to build systems that learn from massive volumes of data, understand hidden patterns, and make underwriting decisions that don’t rely on formal employment history or access to traditional banks. In regions where most people live and earn informally, where addresses are fluid and income is unstable, this kind of “tool” feels like a potential breakthrough. It means we could lend to people who have always been excluded, not because they are irresponsible but because they are invisible to the traditional system.

The idea sounds promising. And it truly is. But it is also dangerous. Because for every loan that AI helps you approve correctly, it can also give you a false sense of certainty about decisions you don’t fully understand. If you’ve ever seen what bad underwriting does at scale, you know it’s not something you want to take lightly. In India, for instance, the early years of digital lending saw a surge in default rates and borrower distress, prompting the Reserve Bank of India to issue stricter guidelines for algorithmic credit. In Kenya, digital lenders using behavioral data models were responsible for a spike in loan defaults and aggressive collections, which led to a major regulatory clampdown in 2022. In Brazil, the central bank had to step in with stronger data privacy laws after AI-based lenders were found to be targeting vulnerable consumers with exploitative credit offers.

What this shows is that while AI offers new tools, it doesn’t remove the underlying problems of credit. If anything, it amplifies them. A bad lending decision made manually affects a few borrowers. A bad decision made by a model affects hundreds of thousands, and it does so quickly, silently, and with almost no room to intervene once it’s in motion.

So yes, AI gives us scale and structure. But that structure is only as good as the assumptions it is built on. And in environments like ours, where those assumptions are hard to pin down, that scale can easily become a risk multiplier.

In the absence of law and data, AI becomes a proxy for judgment.

Let’s start with why AI even matters in our context. In countries where the legal system is functional and the credit infrastructure is well-established, lending decisions are backed by real enforcement. People repay loans not just because it is the right thing to do, but because there is a predictable chain of consequences if they do not. A missed repayment damages your credit score. A poor credit score makes your next loan more expensive, or unavailable altogether. That risk of losing access to financial services creates discipline. It builds a feedback loop that reinforces itself over time. This is how credit markets in the US, UK, and parts of Europe and East Asia manage to work at scale with relative stability.

But in most developing markets, the story is very different. There is no systemic punishment for default. A borrower in Ghana, for example, can default on one lender, switch mobile numbers, and immediately apply for a new loan from another lender who has no way of knowing what just happened. In Nigeria, several microlenders have seen borrowers stack five to six loans from different platforms within the same week. India has had similar issues in the past with shadow NBFCs extending loans to customers with no central record of their borrowing history. The lack of integration across systems means there is nothing stopping someone from gaming the market.

So what has kept lending going in these markets? Improvisation. We’ve made do with whatever signals we can find; Phone metadata, airtime recharge patterns, device models, transaction histories on mobile money and social data. None of these were ever built for underwriting, but we have been using them anyway because there has been no better alternative. What AI does is take these scattered, messy, hard-to-interpret signals and turn them into something more structured. It learns from past behavior and tries to predict future repayment capacity. In doing so, it starts to function as a stand-in for formal systems. If you cannot rely on law or data to tell you if someone is trustworthy, AI becomes the thing that fills the gap.

And that is where the hope comes from. If AI can make sense of behavioral data, especially the kind of data people in informal economies actually generate, then maybe we can finally lend to those who have always been excluded. Across markets, there is growing evidence that this might work. In Kenya, some fintechs have used AI-powered mobile data models to reach rural borrowers with no banking footprint. In India, lenders like CASHe and KreditBee have used machine learning to underwrite first-time borrowers based on social and digital signals. In Brazil, initiatives like the Cadastro Positivo have helped incorporate alternative data into national credit scoring efforts, creating new entry points for low-income borrowers. These are early signs, but they show that with the right models, it is possible to design credit systems for people who were never part of the formal banking sector to begin with.

But we should not get carried away. The entire logic behind this promise rests on a very fragile assumption: that AI can understand intent and behavior better than a human can, and that it will continue to do so even when the inputs are irregular, incomplete, or manipulated. This is not a small risk. Because when you remove enforcement and visibility from the equation, what you are left with is a machine that makes decisions in the dark. And that’s not something you can trust blindly.

AI models don’t just reflect data. They reflect bias, too.

This is where the risk really begins to show. A lot of people still assume AI is inherently objective, that once you train a model on enough data, it becomes a pure decision-making tool that is free from human interference. But that’s just not how it works. Every model starts with a set of decisions made by people. We decide which features to engineer, which data sources to prioritize, how to label the training data, what outcomes to optimize for, and which variables to ignore. All of those decisions come from our own perspective. And that perspective is never neutral.

We’re not talking about deliberate discrimination here. No one wakes up and says, “Let’s design a biased model today.” At Lendsqr, we’ve had to build our own credit models, and I’ll be the first to admit that the features we select are based on our understanding of lending patterns. That understanding is based on our experiences in Africa, or in the lending markets we’re most familiar with. We build based on what we think is relevant. And more often than not, that relevance comes from exposure. If you’ve only worked with urban borrowers who use mobile wallets every day, you’ll probably miss the signals from a rural borrower who earns in cash and barely interacts with digital platforms.

This is the quiet way bias seeps in. The model is not making bad decisions because the math is wrong. It’s making bad decisions because the foundation it was built on is incomplete. If your training data is drawn mostly from salaried, male, urban customers aged 25 to 40, then the model will learn to optimize for those types of customers. It will penalize the ones who fall outside that pattern, even if they are just as creditworthy. In India, studies of algorithmic lending platforms have shown that certain groups, particularly women, first-time borrowers, and people from rural districts, were systematically receiving fewer approvals or worse loan terms simply because the training data did not adequately represent their behavior. Similar patterns have been observed in South Africa and Brazil, where informal workers were either excluded outright or assessed using variables that did not reflect their actual repayment behavior.

The hard part is that this kind of bias is invisible until something breaks. You do not notice it when the model is working within familiar territory. But the moment you try to scale or expand to new customer segments, the gaps begin to show. Someone who looks like a good borrower in the real world gets rejected by the model. Another person who should have been flagged as risky ends up getting approved. These mistakes are not always easy to detect, and they are rarely caught early. Meanwhile, real people are affected. They get denied access to credit, mispriced on risk, or quietly pushed to the margins of the system, all without any explanation they can act on.

This is why we have to be skeptical of the language around fairness in AI. Whenever someone says their model is fair or unbiased, the right response is to ask: according to whom? What data was used to train it? Whose behavior shaped the outcomes? What assumptions were made about what risk looks like? Because fairness is not a default setting. It is a moving target, and it depends entirely on whose reality the model is built around.

The moment it works, we rush to scale, and that’s when it breaks

One of the most recurring issues we face in this space is the temptation to scale too fast. It is almost a pattern at this point. You build an AI model, train it on a well-selected batch of borrowers, maybe a few thousand loans. The results look great. Default rates are low. The predictions match what your risk team hoped for. Everything seems stable. There is a quiet confidence that maybe you have cracked something. And then, without fail, someone suggests that it is time to take it national. Maybe even regional.

That is usually where things start to go sideways. Because a model that works on one population size, geography, or behavioral pattern does not necessarily carry over cleanly to a wider and more complex borrower base. The original sample might have shared invisible traits like device type, app literacy, income sources, cultural context. Those are often not factored explicitly, but the model learns to rely on those patterns anyway. And sometimes, the issue is not even about moving across regions. Models that work on a tribe of people in a geography may not even work for a different tribe within the same geography as well. Even within the same city or state, different communities have different ways of living, spending, earning, and using digital tools. So when you begin lending beyond the test segment, even if everything looks the same on the surface, the behavioral assumptions underneath no longer hold.

Even more concerning is how quickly borrowers learn to adapt. In most developing markets, especially those where regulation is still playing catch-up, customers are not passive participants. They observe, learn and test systems. Once they figure out that a certain behavior improves their chances of approval, like maintaining a regular airtime top-up schedule, or ensuring consistent mobile data usage, they will start replicating that behavior, not because it reflects who they are financially, but because it gets them what they want. And when that happens at scale, the entire model becomes distorted. You start lending based on manipulated patterns, not real ones.

We have already seen this play out in multiple markets. In India, several lenders had to quietly reduce exposure in rural states after realizing that their AI models were handing out credit too loosely. The model had been trained on urban digital borrowers with some stability, but rural borrowers had different usage habits and far less capacity to repay. In Kenya, digital credit platforms that relied heavily on SMS and call metadata began seeing spikes in delinquency once customers realized how to mimic the traits the model favored. The signal became noise.

All of this points to the same conclusion. AI models are not static, they can be tricked. They are sensitive to shifts in context and behavior. So while they can be powerful tools for scaling credit, they require constant observation. You cannot simply switch them on and walk away. You need teams watching the data, testing the assumptions, and checking whether the original model is still valid under new conditions. And when the model starts to fail, you need the courage to pause, retrain, or pull back, even if it means slowing down the growth you were hoping to achieve. Otherwise, the consequences show up in the form of mass defaults, broken trust, and reputational damage, things that are far harder to recover from than the time it would have taken to test carefully.

We don’t even fully understand how this thing works.

AI, especially the kind we now use in credit underwriting, is still very much a black box. We feed it inputs, we observe the outputs, and sometimes we can draw a straight line between the two. But when you go beyond simple models into deep learning architectures or complex ensemble decision trees, that clear line becomes difficult, if not impossible, to trace. You might see patterns in the data. You might even see consistent results. But the exact reasoning behind a specific decision is often buried somewhere inside thousands of mathematical layers, and not even the data science team can confidently tell you what the model was “thinking.”

I have sat in meetings where we were reviewing cases flagged by our AI model, and we would come across a borrower with almost no formal income record, no employment stability, and minimal digital footprint, yet the model marked them as low risk and approved the loan. Everyone in the room would go quiet. We would go back and forth, digging into the features, the weights, the training data. Eventually, we would settle on a vague explanation that the model saw something in a combination of variables, perhaps a stable device usage pattern, regular mobile money inflows, or some signal buried in their geolocation metadata. But the truth was, we didn’t really know.

That lack of clarity is not just a technical concern. In lending, it becomes an ethical one. These decisions affect people’s lives. If someone is denied a loan, they deserve to know why. If they are approved for one they cannot afford, the consequences fall heavily on them and on the lender. Yet we are now in a situation where we cannot always explain our own systems. This is a real problem. In places like Brazil, regulators have already begun scrutinizing AI-based credit scoring models specifically because of their opacity.

We have even seen cases where a single, seemingly harmless adjustment to the data, removing a column of zeroes or changing the format of a timestamp, caused the model to behave in completely unpredictable ways. These are not hypothetical risks. They are the kinds of things that creep up silently and only become visible when your portfolio starts leaking. If your default rates begin to rise and you cannot trace the root cause, then you are flying blind.

The core issue here is that AI, while powerful, is extremely sensitive. It is not robust in the way many people assume. It relies on data that is noisy, incomplete, or sometimes manipulated. It draws conclusions from correlations that may not hold in the real world. And once it begins to drift, it does not send up a red flag, you only know something is wrong when the damage has already begun.

So what do we do? Use AI, but use it with care

I want to be clear: I’m not against AI. Far from it. In fact, I am staking a lot on it because I actually believe it’s one of the most important technological shifts we’ve seen in the push for financial inclusion. In markets where traditional underwriting has always excluded informal workers, students, rural families, and the self-employed, AI gives us a real chance at changing that. It allows us to look beyond bank statements and employment records and build lending models around real-life behaviors. That’s powerful.

But it also demands humility. Because we are not working with perfect systems. We’re still in experimental territory, and pretending otherwise is reckless. When we deploy AI in underwriting, we’re making decisions that directly impact people’s ability to survive, run their businesses, or support their families. That’s not the kind of power you hold lightly. It requires discipline, caution and most importantly, it requires honesty; about what we know, what we don’t know, and where we might be wrong.

That means putting systems in place not just technically, but institutionally. Teams need to monitor models continuously, not once a quarter. Decisions that look off need to be flagged and traced back. When we see drift, we must be able to course-correct quickly, even if it means pausing lending altogether. It also means communicating clearly with your team, your board, your regulators, and the customers whose lives your systems affect. AI doesn’t get a free pass just because it’s complicated. If your model denies someone credit, you should be able to give a meaningful reason, or at least admit when you don’t fully understand it. That transparency is what builds long-term credibility.

I’ve seen too many teams, startups and large institutions alike, fall into the trap of overconfidence. A model works in one market or segment, and suddenly everyone assumes it can be scaled everywhere. But most of us working in emerging markets know that every geography is different. Assuming your model is universally valid without validating it at every step is how things fall apart.

India’s Reserve Bank has already raised concerns about algorithmic bias and lack of explainability in credit decisions. In Brazil and Colombia, fintech lenders are now being asked to justify their use of alternative data and prove their models do not discriminate. These conversations are going to keep happening, and rightfully so. The new EU AI Act is one more reminder that regulators are starting to take these risks seriously. Under this law, credit scoring models that use AI have been explicitly classified as high-risk systems. That means providers will be required to meet strict obligations around transparency, accountability, and human oversight. Whether or not you operate in Europe, this signals where global expectations are headed. And sooner or later, every serious player will need to align with that level of scrutiny. 

As lenders and providers of lending tech, we owe it to ourselves, and to the customers we serve, to approach AI with a mix of ambition and responsibility.

So yes, let’s use AI. But not like it’s infallible, let’s use it with our eyes open, and with the kind of accountability this space actually demands. Because inclusive finance is not just about scale. It’s about doing right by the people we are trying to serve, especially when the systems we’ve built still don’t fully understand them.

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.

I stumbled on a post that said Telegram runs with just 30 employees. Thirty. For a platform serving over a billion people. And I just sat there, stunned. I couldn’t even pretend to process it. My mind was properly mindfucked. I kept asking myself, how? How do you run a platform that huge, with that many features, with barely enough people to fill a small Zoom call?

Then came OnlyFans. Yes, that OnlyFans. Whatever people choose to use it for is irrelevant. What matters is that the company managed to generate over a billion dollars in profit within just three years, with a team of only 42 employees. No bloated org chart and endless chains of middle management designed to make lives miserable. Just a few people, some servers, and insane efficiency. 

Clearly, these companies were not playing by the same rules. They had figured out something the rest of us hadn’t.

You see, I’ve worked in places where excellence wasn’t just a fancy word on a mission statement. People like Aigboje Aig-Imoukhuede and Herbert Wigwe weren’t just exceptional because they worked long hours (they did), but because they were razor-sharp, operated on a high-performance frequency, and demanded the same from everyone around them. It was just the minimum standard and you either leveled up or you got left behind. 

That kind of energy had a way of forcing you to either grow fast or quietly excuse yourself.

Luckily, I got a close-up view of that energy early in my career at Access Bank. That place was, without exaggeration, the most valuable kind of pressure cooker. It gave no quarter to mediocrity. You might have walked in with vague ambition, but if you made it through, you came out with a very clear understanding of what elite execution looked like. It wasn’t just about working hard; it was about operating with sharpness and speed, and pressure. You were expected to make decisions quickly, own your output, and never use bureaucracy as an excuse. It could be intense and occasionally unforgiving, but it was the most formative professional experience I’ve ever had. It taught me what a real high-performance culture feels like.

So when I started Lendsqr, I made a personal decision: we were going to build that same culture of excellence,  but make it smarter, leaner, and automated to the bone. Not just hiring people who could take instructions, but people who could figure things out, solve problems without fanfare, and keep things moving without layers of hand-holding. A place where high performance is the norm, not the exception. Where being technical isn’t a job description but a shared mindset. That’s the only way we were ever going to scale without becoming bloated or mediocre.

No dead weight, no dumping ground.

I’ve always held the view that companies should not become homes for “pity departments”. Those departments that exist just so someone can hold a title and collect a paycheck. These are often the places where underperformers get quietly reassigned, tasked with managing something non-essential, while everyone pretends it’s all part of the plan. It’s the professional equivalent of sweeping dust under the rug. Everything looks neat until you bother to look closely.

At Lendsqr, we don’t make room for that kind of organizational clutter. If you’re unable to operate at the standard required for any team in the company, then you don’t make it in. We don’t insulate some teams from pressure while expecting excellence from others. Whether you’re in Marketing, Finance, HR, Engineering, or Product Operations, the expectations are the same. You must be able to think clearly, make good decisions quickly, and execute without someone constantly guiding your hand.

And yes, that meant making uncomfortable choices about how we evaluate applicants. Academic performance doesn’t tell the full story, but it does show a history of effort. 

We obsess over grades. And also don’t obsess over grades. Now, I’m confused 😵

We’ve hired smart individuals from polytechnics who often outperform those with a first-class degree. Some of our most impactful people didn’t come from flashy schools or big brands. One of our best engineers never finished school but what he did finish was a long list of complex features, urgent fixes, and systems that never go down. That’s what matters here. 

In Lendsqr, we care about whether you can think critically, learn quickly, and do difficult work without falling apart. And we’ve built our hiring process to reflect that. It includes rigorous evaluations and hands-on filters. If you’re not resourceful and ready to be useful, you’re not making it past the first few stages, and that’s intentional.

Everyone builds and automates. No exceptions.

One of the biggest problems I’ve seen in companies is how much time gets wasted on repetition and low-value tasks. You’ll find people spending hours each week copying and pasting the same reports. Others wait endlessly for the data team to pull simple numbers they could easily learn to access themselves. Some won’t touch a broken workflow until engineering steps in to fix it because the companies decreed that tech stuff is best done by tech bros, which reinforces silos and slows everyone down. So we killed that way of working at Lendsqr.

From the very beginning, we agreed that if we wanted to operate like a lean, high-performance company, everyone, regardless of their title or department, is expected to be hands-on. That means being able to access the data you need without blockers. If you need numbers, you learn how to write SQL queries. If a workflow is too manual, you build your own automation using n8n. If something breaks, you check the logs, read the error messages, and try to figure out what went wrong before escalating. You don’t sit around waiting for engineering to solve problems that are well within your ability to handle. Whether it’s drafting and testing HTML emails, playing with APIs using Postman, or simply understanding how different parts of the product talk to each other, we expect everyone to be curious and capable enough to dive in. 

And the expectations don’t stop at technical tools. Even engineers are required to understand the fundamentals of credit risk, because it’s not enough to only build features; you need to understand the domain in which they exist. Since you’re expected to build lending platform for lenders, you must know what good lending looks like. 

Just to give you a perspective, our ML models are written by guys from the Product Ops team and the best SQL writers are not even from engineering. Our finance team would coach an engineering team how to use n8n for automation.

There’s no space for helplessness, because we don’t make room for it. If you don’t know how to do something, you’re expected to learn. “I’m not technical” is not a viable excuse in our culture. Even our HR lead builds their automations to make hiring workflows more efficient. That’s how embedded this mindset is. If you work at Lendsqr, you’re expected to think critically, move independently, and make things better without needing permission. You don’t throw problems over the wall and hope someone else catches them.

Product owners have to be actual owners

At Lendsqr, product ownership means just that: ownership. It’s not a role for someone who only writes tickets or sets up meetings. Being a product owner here means taking full responsibility for how your product area performs; technically, commercially, and from the user’s point of view. They’re not simply there to relay messages between engineering and other teams. They have to think clearly about trade-offs, understand how things work under the hood, and stay close to the business and user needs, as this helps them make better decisions and move things forward without needing constant direction.

This kind of ownership requires a wide lens. Our product owners are expected to speak the language of engineers well enough to collaborate deeply and problem-solve together. At the same time, they have to understand the business strategy, how the product supports growth or efficiency, and how to prioritize based on actual outcomes, not gut feelings or noise. And then there’s the user experience; something we expect product owners to care about deeply. Whether it’s a micro-interaction or a major feature flow, they need to be able to evaluate whether it makes sense, feels intuitive, and delivers value.

To support this level of ownership, we’ve built weekly training sessions into our routine. These aren’t theoretical workshops. They’re practical training sessions based on what we encounter day-to-day. We spend time walking through how to write postmortems, not just so that issues are tracked, but so that context is preserved and others can step in quickly. We go through how to escalate issues intelligently, making sure the right people have the right information. Many of these end up with automations that owners do themselves.

We also train product owners on how to debug small issues enough to avoid bottlenecks and dependency loops when engineering is tied up. That kind of autonomy matters when things move fast. On the planning side, we show how to break down features clearly and structurally, so engineering and design teams aren’t starting from confusion or vague ideas. And we talk about usability; what makes an interface intuitive, how users think, and how to bring simplicity into even the most complex flows.

All of this isn’t because we expect perfection or want everyone to be an expert in everything. It’s because we want product owners who are genuinely helpful and capable of driving execution forward. The less they need hand-holding, the more momentum the entire team builds. And over time, that makes the difference between a product team that’s reactive and one that’s constantly pushing things ahead.

Everyone is an engineer, whether it says so on your job title or not

At Lendsqr, technical skills aren’t confined to the engineering team. While we have a team of skilled engineers who handle the deep, complex system work, almost every other team in the company engages with technology in real, hands-on ways. And that’s by design. Being “technical” here isn’t about what degree you have or whether your role has the word “engineer” in it. It’s about what the work demands and whether you’re willing to level up to meet it.

Take our Product Ops team, for instance. Their job isn’t to be passive intermediaries between customers and engineers. They’re expected to understand how our systems work well enough to build automations, write simple scripts, and investigate issues thoroughly before handing anything off. They know how to trace the source of a problem and propose concrete solutions, not just escalate tickets. The Growth team, too, doesn’t sit around waiting for data to arrive. They query APIs themselves, generate the reports they need, and even troubleshoot issues when things don’t look right. In meetings with customers, they can speak fluently about the system, sometimes so well you’d think they were on the engineering team.

We’ve made it clear that there’s no safe zone in the company where you can avoid learning some level of technical skill. Even people who would never describe themselves as “technical”, including those who started out with zero engineering background, are now comfortable reading logs, writing complex queries, or debugging issues.

We’re not trying to turn everyone into a full-stack developer. That’s not the point. What we are doing is removing the kind of fragile dependencies that slow teams down. When people know how to investigate and fix problems themselves, they move faster. When they understand how the systems behave, they make better decisions. And when they’re confident navigating technical tasks, they become much harder to block or derail.

Why do all this? Because we want to scale without bloat

Lendsqr currently serves more than 2.7 million users and supports the lending operations of over 7,000 lenders. And all of this is being run by a team of fewer than 40 people. That’s not a happy accident or a temporary situation we’re trying to fix. It’s the outcome of a deliberate and disciplined approach to building the company. From the very beginning, we made a decision to grow differently by doing more with less, but do it sustainably and without compromising quality or ambition.

Our goal isn’t to keep hiring more people as we grow. In fact, we’re designing the company so that we won’t need to significantly increase our headcount until we’re serving 10 million users or more. And the only way that’s possible is if the company is designed to be resilient and efficient from the inside out. That means building systems that don’t crack under pressure, designing workflows that don’t rely on a single person’s heroics, and creating infrastructure that can handle scale without collapsing under the weight of complexity.

Instead of throwing more people at every new problem, we focus on solving those problems in smarter ways. We build internal tools that take away busywork and allow one person to handle the kind of operational load that would normally require a team. We invest heavily in training and cross-functional learning so that people are empowered to troubleshoot, experiment, and resolve things on their own. We treat documentation as an asset, not a chore, and we create a shared understanding of how things work so that knowledge isn’t hoarded or lost when someone moves on.

That’s what we’re really building: a culture that survives change. One that doesn’t depend on any one person or team to keep the company running. 

It’s not perfect. But it’s deliberate.

We’re not pretending to have it all figured out, and we’re still 10,000 miles from Telegram-level productivity. But that’s the bar we’ve set for ourselves. That’s the north star we’re aiming at because it forces us to think differently about how we build, work, and grow.

This isn’t about chasing perfection but being intentional. I want to build a company where people feel genuinely challenged, where every role comes with stretch, discomfort, and pressure that pushes you to level up. A place where you can’t just coast. Where the expectation isn’t that you tick boxes or do your part, but that you grow sharper, faster, more useful over time.

And it’s not just about engineers chasing technical mastery. We want a culture where every single person (from Marketing to customer support to HR)  is constantly upping their game, learning something new, and improving how they solve problems. We want curiosity to be second nature and mediocrity to feel unnatural.

This kind of environment isn’t built for everyone because it doesn’t offer a quiet corner to hide in. But the people who thrive here? They leave changed. They build resilience, operate like owners, and leave knowing how to figure things out in any room, any company, and under any pressure. And honestly, that’s exactly the kind of company that’s worth building.

Africa’s credit system is broken, and I’m not talking theory. I’ve sat in rooms with lenders who are bleeding money. I’ve seen smart, hardworking people get locked out of loans for reasons that make no sense. And I’ve watched regulators roll out “financial inclusion strategies” with all the ceremony of a wedding but none of the lasting impact.

We’ve built a credit system that serves the elite, underwrites only the safest bets, and then acts shocked when the economy isn’t moving. Meanwhile, over a billion Africans who need credit either don’t qualify or don’t even know where to start. But that doesn’t solve the fact that someone running a logistics business can’t get ₦200,000 to buy a second bike. Or that a student has to drop out of school over KES3,000. Or that a trader with 10 years of steady income still has to “know someone at the bank” to get a chance.

The credit system is stacked against everyday people. And the main reason is this: lenders don’t have enough signal. They don’t know who they’re lending to. And because they can’t see, they’d rather not risk it.

Open banking, if implemented well, flips this script. It’s the wiring that finally allows financial institutions to understand their customers at scale. It gives lenders the confidence to do what they were designed to do: lend.

Here’s how.

People will stop taking loans in the dark

Right now, most people in Africa get their loans from cooperatives, whether you call them Osusu, Mashonisa, Chamas, SACCOs, or VICOBAs, depending on where you come from.  The problem isn’t just access. It’s discovery. Most people don’t even know what’s available or whether they can qualify. Open banking changes that completely.

Think about it: if your bank transaction history is available (with your permission), then any legitimate lender plugged into the ecosystem can assess you without you lifting a finger. You don’t need to download ten different apps to compare offers. Lenders can now come to you with offers that actually make sense based on your income and behavior. Let’s say you earn R25,000 per month, spend responsibly, and don’t have existing debt. A lender could see that and offer you a R30,000 personal loan at a decent rate. No paperwork. No guesswork. No running around.

Compare that to today, where people Google “instant loan South Africa” and end up borrowing R500 at 25% interest from a shady app that threatens to call your whole contact list. Loan discovery, when powered by real-time financial data, removes the randomness. It puts structure and sense into borrowing. This is how credit becomes normal. Not something for just emergencies or backdoor deals. Just part of everyday financial life.

Lenders can finally know who they’re lending to

Nothing terrifies a lender more than lending to someone who basically doesn’t exist. I’m talking about those applicants with no reliable ID, no financial footprint, no utility bill, no workplace reference… just a phone number and their name. You don’t know their income, you don’t know their spending habits, and if the loan goes south, good luck trying to trace them. They vanish into thin air. And yet, these are the majority of people lenders have to consider every day in Africa.

Yes, we’ve had some steps in the right direction. Things like the Ghana Card, Kenya’s UPI, South Africa’s biometric bank verification, and Nigeria’s BVN. But ask anyone in lending and they’ll tell you: those IDs are only as good as the systems behind them. And those systems are often patchy, out-of-date, or not connected to any real financial behavior.

You can have a national ID and still be financially invisible. That’s where open banking actually delivers something useful. It doesn’t just give you a name and photo. It gives you a live, breathing snapshot of who someone is financially. How much they earn. How often they get paid. Who sends them money. What they spend on. Which accounts they move money between. Whether they’re constantly borrowing from ten different sources just to survive, or if they’re stable and disciplined. All of that becomes visible. All of that becomes verifiable.

And let me tell you, as someone who’s been interacting with thousands of lenders in the last decade: this changes everything. With just one API call you can go from complete blindfold to full situational awareness. You can see if someone’s bouncing around five mobile wallets to avoid paying back loans. You can see if they’re spending 40% of their income on betting. You can tell if that “salary” they claimed is really just random transfers from their cousin.

This isn’t about being intrusive. It’s about trust. If you’re going to hand someone real money, you want to be sure they’re real, too. Open banking finally makes that possible.

So no, you’re no longer lending to just “Kiki from Soweto” or “Ama from Kumasi”. You’re lending to someone whose financial life is visible, traceable, and makes sense. That’s the level of confidence lenders have been begging for.

And with that kind of signal, Africa can actually start to build a real credit system, one where people don’t get judged by where they live, how they look, or who they know, but by what they actually do with their money. That’s the kind of progress we should be aiming for.

Character won’t be a mystery anymore

In traditional banking, the “5 Cs of credit” include character, but good luck trying to measure character without data.

That’s why open banking is such a breath of fresh air. For the first time, lenders can actually see how someone behaves with money. And I don’t mean what they say on their loan application. I mean real, verified, timestamped behavior.

Do they pay their bills on time, or wait for three reminders and a threat? Do they top up DSTV every month or spend the last KES 1,000 in their wallet on betting? Do they consistently save a little something when they get paid, or do they drain their Momo wallet within 48 hours?  Do they send money to their mum regularly? Are they paying off old debts bit by bit? Are they living within their means? These are the signals that show you who someone really is. And open banking puts that in plain sight.

This stuff matters way more than people think, especially in low-income markets. You’d be surprised how many financially responsible people get overlooked because they don’t have fancy payslips or big balances. But give a lender a market woman who earns GH₵800 and repays her GH₵50 microloan every week without fail, and I’ll choose her over a loud Instagram vendor in Nairobi who’s pulling in GH₵100,000 a month and ghosting all their BNPL obligations. One is reliable. The other is noise. And in lending, reliability always wins.

The beauty of open banking is that it helps lenders separate the real ones from the pretenders. Not by guessing, not by profiling, but by watching financial behavior over time. When someone’s character shows up in their transaction history, you don’t need to play detective.

It’s not just helpful, it’s liberating. Because it means lenders can finally build models that reward trustworthiness, not just income. You don’t need to be rich to get access to credit anymore, you just need to be consistent, responsible, and verifiably so.

The capacity to repay becomes obvious

Right now, many lenders are flying blind when it comes to borrower capacity. And I’m not talking educated guesses, I mean “hold your breath and pray” type of lending. You’ll see lenders asking for a payslip from three months ago, or an employment letter from a job the borrower might not even have anymore. Or worse, they just go by “gut feeling”, what the customer looks like, what they say, or what their bank balance shows that day.

But income alone doesn’t tell the whole story. And a static document doesn’t mean much when people live paycheck to paycheck. With open banking, you don’t just know what someone earns, you know what they keep. You see the full money story. You see their income coming in, their bills going out, how much is left by the 20th of the month, whether they’re constantly overdrawn or quietly putting money away. And that’s where the real insight lives.

Let’s say someone is pulling in KES 75,000 a month. Sounds good, right? But when you look closer, they’re spending KES 74,000 monthly on rent, airtime, fast food, and random impulse buys. That’s not capacity. That’s a walking default risk. Meanwhile, someone else is earning R12,000 a month. Not flashy. But month after month, they save R1,500, pay their bills early, and don’t rack up any overdrafts or gambling spikes. That’s someone who knows how to manage their money. That’s capacity.

Open banking exposes these patterns clearly and consistently. No more “Oh, but she works at a bank, so she’s probably responsible.” No more, “He drives a Benz, so he’s good for it.” That’s all nonsense. Plenty of people look financially healthy and are drowning in soft loans and bounced debit attempts. They have a financial cancer that only real financial CAT scans can reveal. Real capacity is in the behavior. And now, you get to see it. For lenders, this is huge. It means fewer defaults. It means you can stop handing out loans to people who are already overleveraged. It means you can start saying yes to people who might not look like ideal borrowers on paper, but who manage their money better than most.

And let’s be clear, this isn’t just about protecting the lender. It’s also about protecting the borrower. You can’t talk about financial inclusion and then bury people in debt they were never equipped to handle. That’s how credit becomes predatory. Open banking gives us the tools to do better. To offer credit based on truth, not guesses. And when we start doing that at scale, that’s when credit starts to actually work for both sides.

Repayment won’t be a circus anymore

Ask anyone who has ever tried collecting loans in Africa; they’ll tell you repayments are where the real chaos lives. Lending money is the easy part. Getting it back? That’s the actual work. You spend weeks underwriting a loan, setting up the disbursement, calling the customer to confirm details. Then repayment day comes, and boom! the debit fails. Why? Because the borrower quietly moved their funds to a different account the night before. Or suddenly became unreachable. Or claimed they “thought the loan started next month.”

If you’re lucky, they pick up your calls and promise to “sort it out before Friday.” If you’re not, you’re sending follow-up emails, WhatsApp nudges, and threatening SMS blasts. Your collections team becomes part debt collector, part therapist.

This nonsense costs real money. Failed debits, retried attempts, manual follow-ups, customer support hours, it all adds up. For smaller lenders, especially, this can break you.

Open banking changes this game completely. Now, borrowers can link verified bank accounts before disbursement. Not just any random account they don’t use, but the account their salary hits, the one where they actually keep money. And once that’s done, repayment becomes a non-event. Lenders can schedule collections with confidence. They know which day the money comes in. They know which account actually has activity. They’re not relying on stale mandates anymore.

And borrowers benefit too. No awkward “Sorry, I forgot” moments. No running to an agent to make manual payments. Just a system that quietly and efficiently does what it’s supposed to do. It also makes gaming the system much harder. That trick of moving money around multiple accounts to avoid debit orders? Dead on arrival. Open banking shows the full picture. You can see if someone suddenly diverted funds elsewhere or is trying to keep their repayment account dry. You see the full transaction flow across banks.

Since we all agree that collections are the heartbeat of any lending business. Fixing collections is therefore a need, not a want.  You could have the best credit models in the world, but if your collections are weak, you’re toast. A strong loan collection system means you can breathe. You can reinvest, grow, and actually build a scalable credit operation.

The cost of lending will finally drop

Loans aren’t expensive because money is hard to find. Money is everywhere. There’s always someone with cash trying to make it grow. What makes loans expensive, especially in Africa, is risk. Plain and simple. Lenders aren’t out here charging 15% per month because they’re greedy (well, some of them are). They’re doing it because they’re guessing. Guessing who the borrower is. Guessing if they’ll pay back. Guessing if that payslip is real or if the borrower has five other loans running.

And when you’re lending in the dark, you build a cushion, a nicer way for what we boringly call a risk premium. A big fat cushion of interest rates, late fees, and hidden charges. Not because you want to, but because you have to. That’s how you survive the chaos. Now, with open banking, you’re not guessing anymore. You know exactly who the borrower is. You see their inflows, spending patterns, loan history, and even how often they buy airtime or send money to their loved ones. You can tell if they’re reckless or responsible. And when repayments are automated, you cut out all the drama that comes with chasing funds.

That’s what reduced risk looks like. And when risk drops, cost follows. It’s not rocket science, it’s just better data. So instead of lenders charging borrowers at 20% interest, they start getting comfortable giving them that same loan at 4%. Not because the government forced their hand, or because people were complaining on Twitter, but because the numbers finally make sense. They have confidence and clarity. That’s how credit becomes affordable. That’s how it becomes scalable. And more importantly, that’s how it actually starts working for the people it was meant to serve.

Credit will become infrastructure for growth

This is the endgame. It has always been my endgame for open banking and Lendsqr. 

When credit becomes accessible, affordable, and predictable, it stops being a luxury for the elite and starts becoming infrastructure. Just like roads or electricity. It powers everything.

Schools can offer tuition loans. Landlords can offer rent payment plans. Businesses can access inventory finance without having to beg. Individuals can invest in productive tools, not just survive. And it’s not just about convenience. It’s about dignity. Because what credit really does, when it’s done right, is give people room to plan their lives. Not just react. Not just hustle their way out of emergencies. But plan. Make decisions based on logic, not luck. Build. Then grow.

That’s the real magic. Not the headlines, not the sandbox launches, not the roundtables where everyone nods but nothing changes. The magic is when everyday people: students, traders, teachers, small business owners, can say: “I know what I need. I know what I’ll get. And I know how I’ll pay it back.” That’s how we unlock scale. Not just for lenders, but for the entire economy.

And that’s why open banking matters. Not as a trend, but as what we need to finally make credit work for Africans in a way that’s sane, humane, and sustainable. If we get this right, we won’t need to hype it. The impact will speak for itself.

But here’s the part everyone likes to ignore

All of this. Every single benefit listed above only works if we don’t mess it up. Open banking won’t magically fix credit in Africa just because we launched some APIs or published a framework. It only works if the entire ecosystem, banks, fintechs, regulators, telcos, lenders, actually behaves like we’re building public infrastructure, not private kingdoms.

That means no more hoarding customer data under the guise of “competitive advantage.”No more regulators sitting on policies for 18 months while innovators suffocate. No more anti-competitive behavior from incumbents who are scared to lose control. And definitely no more silos pretending to be platforms.

Because if we take that route, if we gatekeep access, stifle collaboration, or over-regulate before the engine even starts running, we’ll end up with just another fancy initiative we killed before it had a chance to do anything meaningful.

We’ve already seen what broken credit looks like. Loan apps with 50% default rates. Borrowers running from one lender to another. Lenders pricing loans at 15% a month because they have no data to work with. People choosing between paying rent or buying meds because their salary came late, and no one will lend to them.

That’s the system we’ve been stuck with. And if we’re not careful, we’ll spend the next decade rebuilding the same mess, only this time with fancier jargon.

This is our shot to do it differently. To build something that actually works. To treat credit not as a privilege for the few, but as a foundation for growth. But it won’t happen by default. It will only happen if we choose to work together like grown-ups with a shared goal. We don’t get many chances like this. Let’s not blow it.

I’ve seen enough in this industry to separate pipe dreams from actual products. And when it comes to open banking in Africa, the balance is painfully off. The ideas are loud and ambitious. Everyone talks about transformation, disruption, and leapfrogging. But the infrastructure that’s meant to support all of that? It barely exists. Every year, there’s another fintech conference or digital economy summit. Another white paper from a central bank. Another flashy panel on financial inclusion. The language is always the same: bold claims about innovation, speeches about regional integration, and charts that promise a new era. But when you go looking for the actual APIs? The kind that allows real-time, secure, cross-platform data sharing between banks, fintechs, and other financial players? You find almost nothing. A few pilots here and there. Maybe a sandbox that went live for three months before going quiet. But very little that works in the wild.

Nigeria, despite all the wahala, has consistently led the way in fintech across Africa. It’s one of the few sectors in the country that actually works, and works well. So, in a way, it’s not surprising that Nigeria is also leading the charge for open banking. What might seem surprising to outsiders is that a country where the rules can shift overnight, where economic volatility is part of the fabric, and where regulatory clarity is often a moving target has managed to pull this off. Nigeria is the one African country that has taken open banking from a theoretical concept and turned it into a structured, policy-backed framework. The Central Bank of Nigeria released the official Open Banking Implementation Framework, complete with technical standards, governance models, and defined phases. If the implementation timeline holds, the first real open banking APIs could go live by August or September 2025. That is not just progress. It is a rare case of follow-through in a region that has grown used to stalling halfway.

Now compare that to the rest of the continent. Many countries are still in what can only be described as the “discussion phase.” Malawi, for instance, included a mention of open banking in its national payments strategy. That’s a start, but no more than a paragraph in a longer document. Kenya, widely seen as one of the more advanced digital economies on the continent, is still juggling consultations as part of its National Payment System Vision and Strategy. And Ghana? The Bank of Ghana has made a few encouraging noises about data sharing and fintech regulation, especially through its sandbox program. But when it comes to codified frameworks, technical specifications, or timelines, nothing concrete has emerged. The momentum just is not there.

And to be clear, this is not about who is more economically advanced or who has more polished systems. Nigeria is not ahead because it is better resourced or more efficient. If anything, Nigeria is often operating under heavier constraints. What sets Nigeria apart is its sheer persistence. The country has a reputation for being chaotic, but it also has a culture of figuring things out by force if necessary. There is an underlying stubbornness, a refusal to wait for everything to be perfect. That grit and willingness to keep pushing even when the system drags has moved Nigeria forward on open banking, while others are still reviewing consultation papers. And sometimes, that’s what it takes to break inertia.

Why does Africa even need open banking? 

Because the bar is already on the floor!

Let’s start with the basics. In most African countries, there’s no financial infrastructure to upgrade in the first place. We’re not replacing old systems, we’re trying to build systems that never existed. While other regions debate how to modernize their legacy banks, most of Africa is still grappling with how to make even basic digital finance work reliably. In many places, checking an account balance or making a bank-to-bank transfer still feels like a small miracle.

This is why open banking matters so much here. In Africa, it isn’t just a convenience or a policy experiment. It’s an essential workaround to the deep infrastructure gaps that have held us back for decades. 

In the UK, open banking was designed to force traditional banks to stop hoarding user data and start cooperating with tech innovators. In the US, open banking is being pushed as a way to clean up and modernize their fragmented ACH and legacy systems. But in Africa, we’re not talking about improvements. We’re talking about putting down a foundation so that digital finance can even begin to scale. It’s a chance to build shared rails that anyone can use, whether they’re a bank, a fintech startup, or a solo developer with a product idea.

Take a hard look at the numbers. Out of 54 African countries, only around 26 have functioning interbank transfer systems. That means in nearly half the continent, moving money from one bank to another is either unreliable, slow, or outright impossible. That’s not just an inconvenience. It’s a fundamental roadblock to any meaningful financial inclusion. Imagine trying to build a logistics business in a city with no roads. You can invest in the best trucks, hire the smartest drivers, set up warehouses, and optimize delivery routes. But if there’s no road network to begin with, none of that effort will matter. That’s where we are with financial systems in many parts of Africa.

Yes, mobile money has done a lot to plug some of these holes. M-Pesa transformed payments in Kenya. MoMo is widely used across West Africa. EcoCash has been essential in Zimbabwe. But these solutions were never built for open, interoperable innovation. They’re closed-loop systems, mostly designed to work within their networks. Yes, they work well in their home markets. But try launching a product that works across different mobile money providers, or scaling a fintech app across three or four countries, and you’ll quickly hit a wall. Without common APIs or open standards, every new market requires a rebuild from scratch. And that is not a sustainable path to growth.

Open banking changes that. It introduces a common language for financial services. Instead of everyone building their own isolated systems, it provides a framework where tools, apps, and platforms can actually connect. It makes it possible for a developer in Senegal to build a loan app that works in Uganda. It allows a bank in Ghana to securely share customer data with a licensed credit-scoring startup in Rwanda. It opens up space for real collaboration, where companies no longer have to reinvent the wheel every time they enter a new market. And that’s how you go from a one-country app to a continent-wide platform.

Credit, savings, identity; everything depends on open banking

When we talk about open banking, we’re not just talking about data access. We’re talking about opening up real financial opportunities.

It isn’t just about sharing data between banks and apps but giving people actual access to financial tools they’ve been locked out of for too long. Real access, not just signing up for an app and calling it a day.

Think about everyday life across Africa. A trader in Lagos is saving a little bit every day through a fintech app. A taxi driver in Accra runs their business with mobile money. A student in Kigali buys data and pays bills with a digital wallet. These actions show real money movement and real financial habits. But right now, this information is stuck in silos. It doesn’t travel where it’s needed to build credit or offer better financial products. And as long as data is stuck in silos, people stay invisible to the wider system. They get stuck in the informal economy and are unable to grow.

Imagine if that data could be shared securely and only with permission. Suddenly, that trader can get a small loan to grow their business because their savings prove they can pay it back. The taxi driver can get financing for a new car. The student can qualify for a savings plan or even a small credit line without jumping through endless hoops. That’s the power of open banking: turning data into opportunity.

It’s not just individuals who suffer. Fintechs and digital lenders burn through resources, recreating the same rails in every new market. Also, fintechs are not able to reuse code. If they need to integrate with 30 banks, that’s 30 different complex codes, each one a fresh pain to start with. Without shared infrastructure, each startup is forced to act like a mini-infrastructure company, building everything from scratch. That slows growth, raises costs, and fragments the ecosystem. No wonder scaling is almost impossible.

So what’s holding the rest of the continent back?

If I had to sum it up in one word, it’s execution. Africa doesn’t lack ideas, talent, or vision. We actually have plenty of all those things. But when it comes to getting things done, that’s where the wheels come off.

Look around. We’ve drafted policies, we’ve drawn detailed roadmaps, and we’ve launched some promising initiatives. Take PAPSS, the Pan-African Payment and Settlement System, designed to make cross-border payments simple and cheap. Sounds impressive, right? Yet, when you check the actual usage numbers, it’s barely moving. Then there’s AfCFTA, the African Continental Free Trade Area, signed by nearly every country on the continent. It was supposed to unlock trade and boost economies, but in practice, it’s still trapped in red tape and customs delays. Progress feels stuck in place. This isn’t just about big projects. Even national ID programs and digital government services stall before they reach full impact. The problem isn’t a lack of ambition or ideas. The problem is that following through is tough.

Why? Because execution is hard. It’s repetitive. It’s unglamorous. It requires coordination, discipline, and above all, time. It means you’re still showing up to meetings three years later, still explaining to stakeholders why this matters, still debugging edge cases when the hype has worn off.

Execution also takes patience and stamina. It’s the ability to keep at it even when the buzz around a project dies down, when media stops covering it, and when stakeholders lose interest or faith. In Africa, political cycles and leadership changes do reset progress, making it even harder to see things through. Funding gaps, skills shortages, and infrastructure challenges pile on top.

But here’s the bottom line: no matter how brilliant the plan, if you don’t execute, it’s useless. Without execution, nothing scales. Nothing changes. And that is precisely why most African countries are still stuck talking about open banking instead of building it.

Nigeria’s messy, painful, miraculous journey

Most people don’t realize this, but open banking in Nigeria wasn’t something that came down from the top or was handed over by government officials in some quiet ministry office. It didn’t start with a grand announcement or a sudden government decree. Instead, it grew from the ground up, from the people who live and breathe fintech every day. It started from industry leaders, and, well, some would say championed by yours truly.

We saw the problem, felt the pain, got involved early, built something and refused to let the idea die. 

Back in 2017, a group of us (now called Trustees)  came together and launched the Open Technology Foundation, which later became Open Banking Nigeria. We didn’t just talk; we drafted the earliest technical standards, created frameworks, and began pulling the community together, slowly growing a movement. 

Then came the hard part: convincing the Central Bank of Nigeria to take open banking seriously. We didn’t just approach them once or twice; we went back repeatedly, refining our proposals, answering tough questions, and pushing for concrete action. The CBN didn’t ignore us, and to their credit, they didn’t just listen politely. They got involved. Unlike in many countries where regulators keep their distance from industry efforts, the CBN rolled up their sleeves and collaborated with us closely. It wasn’t just talk; it was practical, hands-on work, sometimes frustrating and slow, but persistent.

Sure, the process has taken time. But it’s not because the CBN was idle or uninterested. They had to manage bigger crises: inflation surges, currency instability, and shocks from global economic events that threatened Nigeria’s financial system. You can’t focus on building new infrastructure when the entire economy is under threat. You don’t feed your child while the house is burning. You put out the fire first.

Now, with the economy stabilizing and attention turning back to innovation, open banking is no longer just a distant idea or policy on paper, but as an infrastructure that’s finally about to launch. This marks a critical turning point for Africa’s financial ecosystem. The promise of open banking is moving from vision to reality after years of hard, unglamorous work. Nigeria’s journey has been messy and painful, but it is also a sign of what is possible when the industry and regulator work together through thick and thin.

If it took Nigeria eight years, what hope is there for everyone else?

Here’s the reality. Nigeria’s journey with open banking is far from perfect. There have been countless delays, missed deadlines, bureaucratic troubles, and plenty of moments when progress seemed stuck or even reversed. The political instability, economic challenges, and sheer scale of Nigeria’s financial system make it one of the toughest places to get something like open banking off the ground. Yet, despite all the delays and detours, Nigeria has still managed to outpace every other country on the continent when it comes to building open banking infrastructure. And that should worry you.

If a country as complex and chaotic as Nigeria can make meaningful progress after nearly a decade of effort, then what does that say about the rest of Africa? It means they are not just behind in the race; in many cases, they are barely even on the track. Many countries remain stuck in endless policy discussions, with plenty of talk but little action. There are grand visions on paper, but few actual projects moving beyond pilot stages. 

And that’s the real tragedy.

Because Africa has everything it needs to make open banking work. The talent pool is deep and growing every year. Fintech startups are sprouting up all over the continent, fueled by young entrepreneurs hungry to change the game. The demand for better financial services is massive, especially from populations underserved by traditional banks. What Africa lacks is not ideas or energy but the will to execute consistently and effectively. It is the discipline to push through the tedious, slow, and often frustrating work that building new infrastructure requires. The humility to copy what already works, rather than trying to reinvent the wheel.. And above all, it is the courage to build systems from the ground up when none exist, even when it means facing tough political and economic realities head-on. Nigeria’s long, messy journey is a reminder that progress is possible, but only when the effort matches the ambition. The question now is whether the rest of the continent is ready to match that effort and move beyond talk into real, lasting change.

Africa doesn’t need another white paper. It needs a working API.

I’ll say it again: Africa desperately needs open banking. Let’s stop trying to impress donors. Let’s stop overengineering solutions. The best thing African regulators and fintech leaders can do is copy what’s already working.

Build the rails, set the rules, and let people build on top of it. Open banking isn’t about catching up to the UK or mimicking Europe. It’s about giving ordinary Africans access to tools that let them save, borrow, invest, and grow. It’s about moving from financial survival to financial progress.

So if you’re a policymaker, here’s your move: steal Nigeria’s homework. There’s no shame in learning from someone else’s sweat, because the alternative is another decade of panels and pilot programs. And we’ve already wasted enough time.