Adedeji Olowe

Africa is a 1.4billion-person market, 20x the size of the UK and 4x the size of the US. You would be a stupid founder to sit in your corner of Africa and not explore.

So, everybody wants to go pan-African until reality smacks them. Market expansion often sounds sexy on paper. It is the kind of announcement that founders like to make with chest-thumping pride, almost the same way politicians love to declare “we are diversifying the economy.” It feels good to say, signals growth and it gets investors nodding and clapping. You can bet the press picks it up, and suddenly you are in the headlines as the next big continental play.

The moment a startup in Lagos, Nairobi, or Cape Town grows to a certain size, the itch begins. There’s this unspoken belief that to be truly successful, you must spread your wings beyond your home country. Suddenly, we all want to plant flags across the continent, to prove we are bigger than just one market.

And to be fair, some companies have actually managed to make it work. Flutterwave is everywhere and has built a name that is recognized across multiple African countries. Paystack has pulled it off as well and has done it with enough discipline that people now point to them as a benchmark. My own company, Lendsqr, has spread beyond Nigeria, working with customers in several countries. Even Chowdeck, which is much newer in the scene, just marched into Ghana and is already crushing numbers like they have been there for years. These stories are inspiring and keep the dream alive for a lot of founders who are planning their own moves.

The truth, however, is that these few success stories sit on top of a mountain of attempts that didn’t end the same way. The continent is littered with stories that don’t sound as rosy. I’ve personally watched Nigerian startups head into other countries with all the confidence in the world, only to retreat quietly when reality hit them. Some leave with public statements about “restructuring strategy” or “shifting focus,” but many just fade out and go silent, nursing their wounds in private. I’ve also seen the reverse. Companies from other African countries have tried to break into Nigeria, hoping to tap into the massive market, and they’ve ended up crashing just as badly.

One example that comes to mind is the Sendy, the Kenyan logistics company that tried their luck in Nigeria. They came in with energy and ambition, but it didn’t last. It was over before most people even noticed they had arrived. Wave, which is doing incredibly well in francophone Africa, hasn’t dared enter Nigeria, and maybe that decision is more out of wisdom than fear. Nigeria is not for children. It eats up outsiders who underestimate it, just as easily as other countries chew up Nigerian startups that come in thinking size and ambition are enough.

So when I talk about the myth of African expansion, this is what I mean. On the surface, it looks like the natural next step in a startup’s growth story. It feels like something you are supposed to do once you are stable at home. But when you look at the outcomes of many who have gone before you, what you find is that expanding across Africa is less of a walk in the park than they let on.

And before anyone runs off with the wrong idea. This is not a dig at any individual founder or business. It is my own reflection from years of watching, living, and sometimes participating in these moves. It is based on the scars I have seen others carry and the ones I have earned myself.

Why do we even want to expand in the first place?

The motivation is never the same for every company, and each founder has their own story to tell about why they chose to leave the comfort of their home market. For me, speaking from my Lendsqr journey, the decision was almost hardwired from the beginning. We never set out to build something that was only relevant to Nigeria. The company’s DNA was global from the very start. Lending has never been a uniquely Nigerian issue, it’s always been a challenge faced in every economy where people need access to credit to move forward. Whether it’s a street vendor in Lagos, an Uber driver in Dubai, or a small migrant-owned business in Toronto, the need for fair, reliable, and efficient access to credit is the same. That understanding shaped how we built Lendsqr and made expansion feel like a natural progression rather than an afterthought.

As things stand today, we already serve customers in countries far beyond Nigeria. We have businesses using Lendsqr in Canada, the United States, Rwanda, Zambia, Malawi, and we are in meaningful conversations with potential clients in several other places as well. That was always the plan. It was never about simply conquering Lagos or focusing on a handful of Nigerian states. The mission was always to solve lending problems wherever they existed, and the more we engaged with different markets, the clearer it became that our solution could travel.

Another major driver is the need to spread risk. Putting all your eggs in one basket is never a smart move, and in a market like Nigeria, it is downright dangerous. If your entire livelihood as a business is tied to the whims of one regulator or one government agency, you are gambling with your future. I have seen this play out in real time. The FCCPC made one sweeping decision recently that threw the entire lending ecosystem in Nigeria into confusion. If Nigeria was our only market, that single move could have ended us. Unfortunately, that’s the reality of building in volatile environments. By expanding to multiple countries, we reduced that risk. It meant that if one market decided to play rough, the entire company would not go under.

There is also the financial angle, which cannot be ignored. Revenue from multiple streams is healthier than relying on a single source, and international expansion makes that possible. If there are markets willing to pay for a product you have already built and tested, it only makes sense to step into them. For us, it was about increasing top-line numbers and also about strengthening the platform itself. Working with a wide variety of customers across different geographies exposes you to different lending cultures, regulatory requirements, and customer expectations. Every time we enter a new market, the product gets better. The feedback loop becomes richer, the technology more resilient, and the overall offering sharper because it has to meet higher levels of diversity.

So when I think about why we wanted to expand, it was never a vanity project or a way to entice investors. Rather, it was rooted in the nature of the problem we were solving, the need to protect the business from unnecessary risks, the opportunity to make more money, and the understanding that the more we stretched ourselves across borders, the stronger Lendsqr would become.

Market expansion is hellishly hard

The biggest reality check for any expansion dream is often regulation. For Lendsqr, we’ve been lucky because we operate strictly as software. We don’t move money ourselves, which means we are not directly under the kind of licensing and compliance requirements that payments companies face. That has spared us many sleepless nights. But for any company whose business model involves actually handling money, the reality is brutal. You will find yourself sitting in front of regulators who can stall you for months, sometimes even years, before you get the green light. The rules are not always clear, and just when you think you’ve ticked every box, another requirement appears. It is never a one-time battle either, be prepared for a constant tug-of-war that drains time, energy, and cash.

From my days in banking with UBA, Access, and Atlas Mara, I saw how different the game is when you are a large institution with the muscle to play. These banks had entire departments dedicated to market entry. The teams were filled with people who spent their entire careers learning how to navigate regulators across different countries. They knew the contacts to call, the processes to follow, and even the cultural nuances that mattered when walking into a government office. That kind of machinery is what gave them an edge. Startups, on the other hand, rarely have that. They move into new markets armed with gist, hearsay and a lot of optimism. And optimism is not a strategy when regulators are standing in your way.

The second wall you crash into is the cost. Expanding into another country is not just expensive; it can bleed you dry if you don’t have the right financial foundation. Banks, again, can afford to raise capital specifically for expansion. They walk into new markets with war chests and stay long enough to weather the storm until their operations stabilize. Startups don’t have that luxury. Many of them try to squeeze international expansion out of funds that were barely enough for their home market. What happens is that the burn rate goes up, revenue lags behind, and very quickly the whole project becomes unsustainable. I have seen promising companies sink this way because they underestimated how much money it would take to break into another market.

And then comes the most unpredictable challenge of all: people. Regulations and money can be calculated, at least to some degree, but people are the wildcards that make or break everything. The hires you make in a new country determine whether your business will take root or wither. Too often, founders underestimate this. They go into a new market, bring in locals, and then realize the work culture and sense of urgency are completely different. Nigerians, for instance, are known for a kind of productive madness (a fancy way of saying we dey craze). We thrive under pressure, we improvise when the ground shifts, and we move with speed even when the environment is chaotic. That edge is what helps us survive. But when you enter a market where the pace is slower, or people prefer caution and safety, and you build your team around that, the disconnect becomes dangerous. You may find that no matter how hard you push, things move at a crawl, and eventually, you drown in that sluggishness.

I witnessed this dynamic back in my UBA days. We were fortunate in countries like Ghana, Cameroon, and Uganda, where we found incredible people to build with. These were competent hires that were relentless, sharp, and willing to fight for results. They would have excelled anywhere in the world, and UBA was lucky to have them. That kind of talent is rare, though. Most startups expanding across borders do not always strike gold when hiring, and without that quality of people on the ground, even the best product and the best intentions collapse under the weight of local realities.

Why banks sometimes win where startups fail

Banks, despite all their layers of bureaucracy and the sluggish pace they’re often accused of, have one advantage that tilts the game in their favor. They don’t always walk into a new country blind or start laying bricks from the ground up. More often than not, they take the shortcut of buying into an existing business that’s already running in that market. It could be a small local bank or a mid-tier institution, but the point is that they inherit something that is already moving. Even if the integration process is messy, full of cultural clashes, and expensive in ways that only bankers can stomach, there is already money flowing in. That immediate revenue, no matter how modest, acts like a shock absorber. It cushions the blows that come with learning a new market and keeps the business afloat long enough for them to figure out their rhythm.

Startups almost never have this kind of luxury. The reality is that we are too strapped for cash to go around acquiring companies, so the default mode is to build from zero and hope it sticks. A handful of acquisitions do happen in the startup world, but those are exceptions and not the rule. Without that initial cushion of ready-made revenue, every mistake cuts deeper and every delay is costlier. Bloodbath is exactly what happens when the burn rate collides with the slow grind of market entry. For startups, survival often comes down to how long you can keep going without oxygen, and in new markets, that is rarely long enough.

What it really takes to succeed across Africa

If anyone is serious about expanding across the continent, here’s what I’ve learned over the years, both from my banking days and now at Lendsqr.

The first thing is to know the market inside out. And I don’t mean a few reports or the stories you hear at conferences. I’m talking about the messy, often uncomfortable details that don’t make it into slide decks. You need to understand how politics shapes business in that country, what regulators actually like to deal with, the unwritten rules that determine who gets ahead, and the local players who quietly control the ecosystem. These are things you only uncover if you’re willing to dig, listen, and sometimes learn the hard way. Expansion is not a place for too much guesswork or improvisation.

Second, you need to bring in people who live and breathe regulation. If your business touches money in any way, you cannot afford to wing it. Regulators have no sympathy for startup ambition, and they will not bend the rules because you have a great pitch deck or you’re coming to solve a “problem”. This means hiring the right experts, even when they don’t come cheap. The truth is that these are the people who can keep your business alive when a new law drops or when the regulator decides to make an example of someone. Paying for that knowledge upfront is a lot better than paying in lost revenue and endless delays later.

Third, you have to be ruthless about the people you hire. Expansion is not the time to surround yourself with people who just like the idea of working with the next “big startup”. You need people who are hungry, who can operate in chaos, and who have the stamina to build something from scratch without constant handholding. These are the kinds of hires who will stay focused when things get ugly and who won’t buckle under the pressure of setbacks. Without them, the whole thing collapses before it even takes root.

Finally, you need to send in people who already understand your culture at the core. Back in banking, the playbook was clear: the first person deployed into a new country was almost always Nigerian. The reason was simple. They carried the DNA of the parent company. They understood how decisions were made at headquarters, they could replicate that culture in a new environment, and they acted as a bridge between home and the new market. If you parachute in someone who has no sense of your company’s way of working, no matter how competent they look on paper, you’ll struggle to translate your mission into reality. Culture is fragile, and expansion has a way of breaking it if you don’t guard it carefully.

So, is African expansion really a myth?

Looking at the stories around us, the evidence leans heavily in that direction. For every Flutterwave, Paystack, or Cellulant that manages to pull off multi-country expansion and make it look effortless, there are dozens of startups that attempted the same thing and quietly disappeared after burning money and energy. The failures don’t get panel discussions or press releases, but if you’ve been in the ecosystem long enough, you’ve seen them. Some shut down entire operations, others limp back to their home markets, and a few keep hanging on in silence, never quite breaking through.

The dream of spreading across Africa carries a certain romantic appeal. It feels like destiny to be the company that unites fragmented markets under one product, to prove that borders don’t matter, and to boast about operations in half a dozen countries. But the ground you’re walking on is unpredictable and often hostile. It takes deep capital, endless resilience, and a team that can withstand constant turmoil. Without those, expansion is less of a growth story and more of a slow-motion collapse.It can be done, but the bar is much higher than founders like to admit. The continent doesn’t reward undercapitalized businesses that expand just because. If you’re not ready to spend heavily on regulation, local talent, infrastructure, and the inevitable mistakes that come with learning new markets, you’ll be finished before you even make it to stability. The idea that “Africa is one big market” sounds nice in pitch decks, but in practice it’s an illusion. Every border comes with its own politics, rules, and players, and pretending otherwise is the fastest way to ruin.

I have no problem with consumer protection. In fact, it’s something I’ve been waiting to see for years in Nigeria and across other African countries. For the longest time, the lending industry here has been like the Wild West, with very little in the way of rules or safeguards for ordinary people. Kenya was one of the first to put clear regulations in place for digital lending, introducing the Digital Credit Providers Regulations in 2022, which set standards for licensing, fees, and borrower rights. So when Nigeria’s FCCPC rolled out its new law on lending practices, I genuinely felt some relief. It finally looked like someone was stepping in to bring order to the chaos and give borrowers the confidence that they weren’t completely on their own. And if you know anything about how Nigeria works, you’ll understand why this feels like such a big deal. We’re used to institutions looking the other way when it comes to the struggles of everyday people, so seeing a regulator actively plant itself in the corner of borrowers deserves some credit. The FCCPC earned its applause on that one.

But once the excitement settled, I had to pause and really think about what this all means in practice. I’ve spent enough time in this space to know that even the best-intentioned laws can take on a life of their own. Protecting borrowers makes sense, and there’s no debate about whether it is necessary, but the way people actually use those protections is where things start to get murky. What I’ve started to notice is a creeping attitude among some borrowers that these rules are now a shield to hide behind, almost like a get-out-of-jail-free card. Instead of seeing consumer protection as a safety net against abuse, some are treating it as a licence to misbehave. And to be clear, I’m not speculating here, I’ve seen it happening already.

This is the part that worries me, because once people realise that the system tilts heavily in their favour, the temptation to game it becomes too strong. Borrowers start defaulting with a straight face, brushing off their obligations as if the law has given them cover. They conveniently forget that every unpaid loan doesn’t just hurt a lender; it also makes credit harder and more expensive for everyone else who might genuinely need it. That’s the danger of a one-sided protection model. It begins with noble intentions, but if left unchecked, it ends with a broken system that serves no one well.

Africa is catching up on consumer protection finally

Nigeria is not the only country waking up to this conversation. Across the continent, regulators are starting to take a harder look at how lending works and what protections borrowers deserve. In Kenya, for example, the Central Bank of Kenya amended its regulations in 2021 to bring digital lenders under direct supervision, requiring them to disclose all fees upfront and banning the practice of debt shaming. South Africa has had its National Credit Act since 2005, which created the National Credit Regulator and set rules around affordability checks, fair interest rates, and even the right for borrowers to challenge unfair credit agreements. Ghana recently passed the Borrowers and Lenders Act in 2020, which established a legal framework for credit agreements and gave the Bank of Ghana authority to license and monitor lenders. Even in Tanzania and Uganda, where digital credit is newer, you now see requirements for lenders to register with central banks and follow rules on interest disclosures. For a region that has historically left borrowers at the mercy of whoever had the cash, this growing wave of credit-specific protections is a real shift, and it is encouraging to see it spreading.

Where the challenge begins, however, is in how these protections are interpreted once they leave the pages of regulation and meet real people on the ground. On paper, the goal is simple: defend borrowers from exploitation. Regulators are standing up and saying, “You will not be cheated, harassed, or bullied when you borrow money.” That is a noble and necessary message. But human behaviour is never that straightforward. Once borrowers realise that there is now a safety net that can shield them, some start to test the limits. I have personally seen situations where people take loans with no serious plan to repay, and when the lender comes knocking, the borrower boldly leans on the regulator’s rules as cover. Loan obligations suddenly become optional, something to get around if possible, since the law is assumed to be firmly on their side.

This is where I start to get uneasy, because protections that were designed to restore fairness can easily turn into weapons for irresponsibility. When borrowers begin to act like regulators are there to punish lenders on their behalf, the balance of the system gets lost. The slope is gradual but very real: a few defaults ignored here, some abuse of the rules there, and before long, borrowing culture itself becomes toxic. If this trend continues, it will not just be lenders who suffer. The very people these laws are meant to protect will find that credit dries up, because no one is willing to take the risk anymore.

Borrowers aren’t always the saints in this story

The uncomfortable truth is that many of the ugly practices we see in lending did not just appear out of thin air. When a lender blasts someone’s contact list or sends threatening messages to their relatives, as crude and damaging as those actions are, they usually stem from a borrower who has refused to repay what they owe. I am not excusing those tactics in any way, because they harm the industry, destroy trust, and ultimately push regulators to come down harder on everyone. But if we are going to have a serious conversation about consumer protection, we cannot skip over the fact that a huge number of these conflicts begin with a loan that was never repaid.

Yes, there are absolutely lenders who operate in bad faith. Some deliberately sneak in hidden fees, inflate interest rates, or design repayment schedules that they know will trip up the borrower. Those businesses are exploitative, and they deserve the scrutiny regulators throw at them. But focusing only on bad lenders tells only half the story. There is another reality, one that regulators rarely talk about: borrowers who collect loans with no genuine plan to pay back. Over the years and even more recently than you’d think, i’ve come across borrowers who take out multiple loans from different digital platforms in the same week, juggling them as if it were free cash. Some default the moment the funds hit their account, knowing that recovery is messy and regulators will step in if the lender pushes too hard. These are not isolated cases, they are patterns that chips away at the very foundation of credit.

If consumer protection is to be credible, it cannot just draw lines around lenders while leaving borrowers free of responsibility. A system where only one side is accountable is already broken. Borrowers need to understand that protections are meant to shield them from abuse, not from responsibility. If you take out a loan in good faith but run into genuine hardship, then yes, you deserve protection, mediation, and even restructuring options. But if you deliberately game the system, hiding behind regulations while treating repayment as optional, you poison the pool for everyone else. What eventually happens is that lenders start pulling back, tightening requirements, or leaving the market entirely, and the very people who lose out are the honest borrowers who suddenly have fewer or no options left.

This is why I believe consumer protection has to be framed as a two-way street. Borrowers cannot keep expecting regulators to fight their battles while ignoring their own obligations. If regulators really want to build a healthy credit culture, they need to make it clear that protection is earned through good faith. Those who exploit the system should face consequences, just as much as predatory lenders do. Otherwise, we are left with a system that rewards irresponsibility and punishes those who are actually trying to do the right thing.

Why digital lenders exist in the first place

The rise of digital lenders in Africa did not come out of nowhere. It was born out of a frustrating reality that traditional banks created and then ignored. Walk into a commercial bank in Senegal and try asking for a personal loan, and you’ll quickly discover that the system was never built with ordinary people in mind. First, you face a mountain of paperwork. Then, there’s the demand for collateral that you don’t have. After that comes the long back and forth that can drag on for weeks, with no guarantee that you’ll ever get the money. And most times, after all that trouble, the final answer is still no.

This story repeats itself across the continent. In Kenya, if you need cash urgently on a Saturday evening to rush someone to the hospital, you already know the bank will not even pick up your call. In Zambia, if a medical emergency strikes on a Friday night, you’re fucked. In Senegal, when school fees are due at the end of the month, banks are not lining up to help parents meet that deadline. For small businesses, it is even worse. An SME founder trying to raise quick capital to keep operations afloat in Nigeria is simply on their own.

The only people answering in those moments were digital lenders. They built a system that actually shows up when people need money the most, whether it’s for survival, school, or business. And that is why borrowers turned to them in their millions. Using mobile phones and tech, they made credit accessible to people who had never had the chance before. SMEs could buy inventory without waiting for a loan committee. Parents could pay fees at the very last moment. Families dealing with health emergencies could find help, even at night.

And this is why the conversation around consumer protection matters so much. When regulations lean too heavily toward shielding borrowers without balancing accountability, digital lenders start asking themselves whether the risk is even worth it. Running a lending business is not charity, and when repayment becomes uncertain while rules make recovery nearly impossible, many lenders eventually leave. Regulators may celebrate wins on paper, but the people who end up stranded are the same borrowers they wanted to protect. The parent looking for school fees will have no options. The small business needing working capital will be left to plead with banks that never wanted them in the first place. That is the irony: in trying to protect borrowers, we risk cutting them off from the only credit they have ever had access to. 

If I were in charge

The harsh truth we must all come to terms with is how consumer protection should never be one-sided. It cannot work if it’s built on the assumption that lenders are always the predators and borrowers are always the prey. The reality is much more complicated. Borrowers absolutely need safeguards against lenders who charge outrageous fees, hide terms, or harass people during collections.

Those practices wear down trust and make credit dangerous instead of useful. But protection also comes with responsibility. If someone takes a loan, there has to be a clear understanding that repayment is not optional. You can put all the protective barriers in place for fairness, yet the foundation of credit still rests on the borrower keeping their word and paying back what they owe.

I would even go as far as saying regulators should create a mechanism for lenders to flag habitual defaulters. If lenders can be blacklisted for bad practices, why can’t serial defaulters face the same thing? Accountability should cut both ways. Otherwise, we are just encouraging a culture of irresponsibility that eventually destroys the credit system.

Now, don’t get me wrong. If I were running the FCCPC in Nigeria, or the equivalent bodies in Kenya, Ghana, or South Africa, I’d still enforce every single protection against abuse. Nobody deserves to be bullied, humiliated, or trapped in unfair loan terms. But alongside that, I’d add a firm and practical layer of borrower accountability. That means making sure consumer protection cannot be twisted into a free pass for irresponsibility. If lenders are expected to meet clear rules and standards, borrowers should too.

Because at the end of the day, the role of a regulator is to keep the system fair for both borrowers and lenders, not to tilt everything in favor of one side. Lenders will only continue to lend when there is confidence that what goes out will come back. And that requires telling borrowers, in no uncertain terms, that protection is available, but it comes with responsibility. If you take a loan, you must be ready to pay back.

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.