Month: July 2025

I’ve heard it more times than I can count. Nigerians, especially those from Lagos, complaining about how slow other Africans are. It usually comes with that familiar tone: a mix of irritation and disbelief, and underneath it, this subtle sense of “we know how to move, these ones don’t.” We say it in meetings, at airports, at conferences, sometimes even to their faces.

The moment you land in Dakar or Kigali or somewhere that isn’t Nigeria, it starts. You get to the airport and the immigration officer is taking their time. The driver comes ten minutes late but isn’t even apologetic. You go to the hotel restaurant and the food takes 30 minutes. And somehow, in your head, everything starts to feel wrong. “Why are they walking so slowly? Why is nobody in a rush? Why do they close shop at 5pm?” You start itching to take control. And before long, you arrive at the usual conclusion that these people must be slow.

But that’s the thing I’ve had to rethink over time. Are they actually slow, or are we just… mad?

Because when I really sit with it, when I look back at all the years I’ve spent working across different African countries meeting regulators, building teams, chasing timelines, trying to get products into new markets, what I see is not slowness but rather a different rhythm, shaped by people who have not made constant urgency a central part of their daily lives.

The truth is, what we Nigerians call “slow” is often just how the rest of the world moves. We’ve gotten so used to chaos and pressure that anything short of that feels like a problem. But it’s not. It’s just… normal. We’re the ones who have been conditioned to treat every task like a fire drill. And when you’re wired like that, calm can start to feel like incompetence even when it’s not.

The numbers tell a story too. Nigeria is the most populous country in Africa, with over 230 million people. Lagos alone has more people than the entire nation of Botswana. In 2023, Lagos recorded an average daily traffic time of over 75 minutes, one of the highest in Africa. This statistic is more than just transportation delays; it represents hours of accumulated stress, pressure, and mental exhaustion. Add to that the persistent power supply challenges, with the country generating less than 4,000 megawatts for the whole country on most days, alongside deteriorating infrastructure, a rapidly growing youth population competing for limited opportunities, and one of the continent’s highest unemployment rates. When you put all these factors together, it becomes clearer why the feeling of urgency has become as essential to Nigerians as breathing. You begin to understand why Nigerians have to hustle for everything. Nigerians wake up and their day already feels like war. So they push themselves to move fast. Not because it’s more efficient, but because the system is broken and moving slow could mean missing out on many opportunities, however small it may be.

Most of this madness we carry around has its roots in Lagos

The way we work and push ourselves, and the way we react to stress and pressure almost without thinking, all come from the kind of conditioning that living in Lagos forces on you. There is something about the city that doesn’t just ask you to move quickly, it demands it. You cannot live in Lagos and stay soft. The city will stretch you, squeeze every ounce of patience out of you, and still expect you to show up the next morning and perform at full capacity.  Else, it ejects you for non-performance.

People talk about Nigerian energy, but even within that, Lagos has its own kind. It is louder, moves faster, feels more aggressive, and carries a level of chaos that stands apart. In all honesty, this goes beyond the pace of work or the level of ambition. Even though the environment itself is designed in a way that keeps you on edge from the moment you wake up. 

You open your eyes and the problems are already waiting for you; The electricity is out, the generator won’t start because the fuel you bought last night was diluted, the roads are heavily flooded, and the candidate you are supposed to onboard for a project is stranded somewhere in traffic that has barely moved for hours. 

At the same time, a dissatisfied customer has taken to Twitter to publicly call out your business, demanding an immediate solution and you are expected to fix the issue immediately and respond with perfect grammar. This is not a rare day, it’s a typical Tuesday in Lagos. And somehow, you get through it. Then you wake up and do it all over again. Eventually, you stop seeing it as chaos. It becomes your new normal.

So when you take that Lagos kind of energy and try to apply it in a city like Gaborone or Lusaka, where people are not constantly running from one fire to another, the contrast is hard to ignore. You move with the same sense of urgency, and everyone around you looks like they are on vacation. You send five follow-up emails in one afternoon and think you are being thorough. Meanwhile, your colleagues are wondering why you are so intense. The problem is not that they are slow,  you are simply running on survival instinct and expecting everyone else to match you.

This is where a lot of us get it wrong because we assume Lagos is the benchmark for productivity, yet the reality is far from that. Lagos is not a model anyone should try to replicate; it is a pressure cooker that forces you to build a kind of stamina you should never have needed to build in the first place.

The systems do not work, so you become the system. The urgency you carry around is not always driven by excellence. A lot of the time, it is driven by dysfunction. When you have spent years operating inside a storm, you start to see peace and stability as laziness. You think if someone is not stressed or constantly multitasking, they must not be serious. But that is not true. That is just what Lagos (in extension, Nigeria) has taught us to believe.

The real reason Nigerians seem to “shine” abroad 

People like to say it is because we are naturally hardworking. That Nigerians are just born with this extra capacity for hustle. It has almost become folklore; this belief that we have something in our DNA that makes us succeed wherever we go. But the truth, at least from what I have seen and lived, is far less romantic. Nigerians who manage to leave the country are not just regular citizens. They are people who have already survived a brutal filtering process before ever stepping on a plane. The average Nigerian abroad is not there because of luck or chance. They are there because they fought their way out.

If you have ever tried to leave Nigeria, you know what that path looks like. You first have to survive the education system, with all its dysfunction. You have to deal with university strikes, missing transcripts, lecturers who ghost you for months, and final year projects that stall for no reason. After that, you enter the endless web of applying for visas, getting bank statements that look strong enough, and trying to convince a foreign embassy that you are not a flight risk. All of this while earning in naira but paying in dollars or pounds or euros. Then there is the task of translating your Nigerian qualifications and experience into something that makes sense in another country. You have to explain your second class upper in a way that does not make you sound unserious. You have to repackage your NYSC year so it sounds like real work experience. By the time you finally board that plane to Toronto or Berlin or anywhere else, you are not just leaving the country. You are carrying the residue of everything you had to overcome to get that far.

This is why we tend to show up differently abroad. We are used to pushing. We are used to things not working and having to find a way regardless. It is not magic, it is survival instinct. And that instinct is usually very strong in the kind of Nigerians who manage to leave. So yes, we often come across as driven, as resourceful, as intense. But we need to be careful not to mistake that for being better than anyone else. What we are is a product of pressure. Nigeria squeezes you, and the ones who do not break end up looking like outliers.

The other side of it, though, is that when you are used to dysfunction, you carry that energy everywhere. You get into systems that are stable, and you start overreacting to things that are perfectly normal. You do not know how to take your foot off the gas. You keep scanning for problems, even when there are none. And that kind of energy, while useful in high-stakes environments, can become a liability when calmness and collaboration are what is actually needed. So yes, we shine abroad, but we also need to admit that we shine with a kind of restlessness that did not come from excellence alone. It came from a system that forced us to always be on edge.

 Other Africans are not slow 

What they have, more often than not, is a clear sense of boundaries. And to be honest, I find myself admiring that more than I like to admit. I envy the people who can shut their laptops at 5pm without feeling like they are committing a crime. I envy the ones who do not feel the need to be permanently available, constantly stressed, or always a few steps ahead of a problem that does not yet exist. I envy the managers who can get work done without turning every task into a dramatic performance. I envy the teams that can walk into a 10am meeting without carrying the emotional weight of ten unfinished items from the night before.

What we often label as slowness is really just balance. People in other African countries seem to have held on to something we have lost, which is the idea that work is important, but it is not everything.

They value rest and maintain clear working hours, and when a task shifts by a day, they do not respond with panic but instead adjust without seeing it as a failure or a sign of poor performance. In many of the countries I have had the pleasure of interacting with, you can feel the difference. People still take lunch breaks without shame. They do not brag about working on weekends, or try to impress anyone with how tired they are. And that, in its own way, is a kind of confidence we don’t talk about enough.

For Nigerians, this is difficult to understand. Our default mindset is that if something is not urgent, it is not important. We have built entire work cultures around pressure and last-minute delivery. If you are not panicking, people assume you are unserious. If you are not calling people after hours, they think you do not care about the outcome. We wear exhaustion like a badge. And we expect others to do the same. So when we meet people who are firm about their time, who are not constantly available, who prioritize recovery and mental space, it annoys us. We think they are dragging their feet. But they are not. They are just refusing to live in crisis mode.

This is not to say there are no hard workers outside Nigeria. That would be unfair and untrue. People everywhere work hard. But in some places, they have figured out how to do that without losing their minds. They have systems that support them, and they have made peace with the idea that work will always be there, but they do not have to be consumed by it. So when Nigerians step into those environments, we often mistake that peace for inefficiency. What we call slow is actually just people refusing to burn out. And maybe we should be learning from that instead of judging it.

But let’s not pretend all Nigerians are high-speed machines

It would be misleading to suggest that every Nigerian operates at the same level of urgency or intensity. I know I’ve painted a picture of Nigerians as relentless go-getters, constantly moving, always chasing the next big thing, but that is only one part of the story. Nigeria has plenty of people whose pace is much slower, and sometimes painfully so. If you have ever tried to renew a passport or register a business, you already know what I mean. Walk into any government office and you will be met with a kind of slowness that feels almost deliberate. People stroll into work late, take long breaks, and act like every simple task is a complicated favour. In those spaces, urgency is treated like a nuisance rather than a standard.

It is not just in the public sector. The armed forces is another place where things move with a kind of bureaucratic drag. Even outside formal institutions, there is a whole segment of Nigerians, often from the older generation, who still approach work and time the way it was done decades ago. Meetings that could have been emails still happen. Technology is avoided where possible. The pace is slower, expectations are lower, and sense of urgency we see in younger, urban Nigerians is largely absent.

All of this is to say that Nigerians are not a single type of person. We are not all hyper-driven or obsessed with results. What has happened, though, is that the Nigerians who get the most visibility tend to be the ones who are operating at that extreme level. The startup founders, the scholarship winners, the tech bros, the creatives making waves in London or New York; these are the people who get noticed. Their stories are amplified, and they become the poster children for what it means to be Nigerian. And while their hustle is real and impressive, it is not the full picture.

There are millions of Nigerians who are not pushing themselves to the limit. They are not building anything groundbreaking, and they are not breaking into global spaces. They are just doing what they need to do to get by. And that is perfectly fine. Unfortunately, we tend to overlook this group whenever conversations about Nigerian energy take place, focusing instead on the more visible and intense examples that dominate the narrative.

We pretend the exceptions are the rule, and we let the loudest voices define the entire identity. But if we are going to be honest about who we are, we have to admit that the full spectrum includes all kinds of people; from those who move quickly to those who take their time, from those who push relentlessly to those who are satisfied with less, and from those who speak loudly to those who operate more quietly.

So what’s the takeaway?

Every country builds its own way of working. Each one develops its own rhythm over time, shaped by history, systems, values, and the day-to-day realities of life in that place. What feels urgent in one country may feel unnecessary in another. What looks like calm in one place might look like delay to someone else. Nigeria’s rhythm, especially the version that comes out of Lagos, has become fast, intense, and often chaotic. It works well when you need to move quickly, solve problems on the fly, or adapt to constant disruption. But it also burns people out. It creates stress that is so common we do not even notice it anymore. It makes it hard to slow down, even when slowing down would lead to better outcomes.

This kind of energy does not always translate well when Nigerians interact with other African countries. When we enter spaces where things move more steadily, we often become frustrated. We assume that if something is not happening fast, then something must be wrong. We try to push others to adopt our tempo without first understanding theirs. We judge rather than observe. And in doing so, we fail to see that our way is just one of many. It is not always better, just louder.

It might be time we stopped seeing ourselves as the standard that others need to meet. Nigerians are not superior because we move quickly, and other Africans are not inferior because they choose to move differently. What we call madness in ourselves is often a response to chaos. What we call slowness in others is often just the presence of structure. The truth probably lives somewhere in the middle.

I think we could all gain something by recognising that. Nigerians could learn to slow down just enough to breathe, to think more clearly, and to stop assuming everything must happen immediately. Other Africans might benefit from picking up a bit more urgency when it matters, not because they are wrong, but because there are moments when speed is necessary. Neither side has the complete answer. But between the intensity of the Nigerian hustle and the steadiness of other African work cultures, there is a space where things can actually work better for everyone.

At the end of the day, most people are doing the best they can in the environment that has shaped them, and while some of us were taught to sprint, others learned to move with more deliberate steps, which does not necessarily make one approach better than the other but simply shows that they are different, and if we stop trying to fix one another and instead take the time to listen, we might find there is something useful to learn.

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.