Month: September 2026

Whenever the conversation turns to people who do not repay loans in Africa, the answer most people reach for first is poverty, followed by the idea that the loan was too large for the borrower to carry in the first instance, and after a while someone suggests the borrower was taken advantage of by a lender with harsh terms. 

I understand why these explanations are popular, because each of them comes with a story one can easily picture themselves in considering the economic reality of the average African. I’m not too far gone to admit that indeed some of these claims truthfully apply to a number of borrowers, and I would never wave those cases away.

Be that as it may, my polarizing view comes from years and years sitting on loan data from many lenders, daily interaction with these lenders and borrowers alike. In the last decade I have had the privilege of supporting thousands of credit providers with technology to power their lending which means I have watched repayment behavior across a very large number of portfolios and over a long stretch of time. 

Having seen it first hand, I can say that practically 95% of the defaults I have come across have nothing to do with a borrower being taken advantage of or having no money. Most people who default don’t want to repay the loan at all, and once you accept this, a lot of the behavior lenders complain about starts to make sense.

A bad credit score costs more in some countries

The reason unwillingness works so well for borrowers in emerging markets is that there is very little consequence for not paying a loan. In the US and in most developed countries, a person who stops repaying a loan watches their credit take a beating, and the damage follows them into ordinary parts of life. Renting a house becomes a problem because whoever is financing it will check your credit history. Forget about even purchasing a house; the banks will nicely usher you out of their offices. Buying a car runs into the same wall, and a whole list of other things you would like to do becomes harder or impossible until the record improves. Those societies are built on credit, so once your credit record is in bad shape, a large part of your ability to function goes with it.

Africa and other emerging markets are a different situation entirely. We have credit bureaus, and lenders can report to them, but the foundation of the society itself was never laid on verifiable credit, so a damaged record seldom stands between a person and whatever they want to do next. Where consequences are this thin, people test the limit, and this has nothing to do with whether someone is poor, middle class, or rich. Every single person, at every income level, is always looking for how far they can push a situation before it pushes back, and a lending system with no memory gives them a lot of room to push.

Ask the people making the collection calls 

I know all this because I work with lenders who sell loans to many people, and when repayment time arrives those lenders are on the phone chasing money. Borrowers get called repeatedly, tempers flare on both sides, and plenty of them shrug it off with the observation that everybody owes somebody, since even the government is owing money. If someone with an outstanding loan goes to apply for another one somewhere else, very little stops them, and that says a lot about how the system treats a person who has already walked away from a debt. What finally gets a borrower to pay tends to show up when it is time to travel.

A friend of mine works in a commercial bank, and he told me about something interesting he found when he moved into a senior role at the bank. As part of his new role, protocols existed that meant he had to interact with records of people who owed the bank money and on one of those interactions, he came across a familiar name of a man whom he knew personally who had a flamboyant or as Lagosians would call it jaiye-jaiye lifestyle, but owed the bank an unspeakable sum of money.  The bank of course had reached out to him on different occasions, but he showed no interest in paying, so the debt sat on the books for years. Until the japa wave hit him and he had to move his family to Canada, unfortunately for him this meant credit history issues AKA ghosts from the past had come to visit. It came as no surprise to no one when the man who barely returned the bank’s calls suddenly paid off his loan in one go.

I have watched this pattern repeat again and again and again. Many people who plan to go back to school for a master’s degree get told by friends and colleagues to go and pull their credit report first, and within days a good number of them develop a strong interest in settling loans they had ignored for years. Their income was the same the week before as the week after, so the money to pay was always somewhere within reach, and the thing that brought about the sudden change was a consequence with a date attached to it.

Fixing it starts with the bureaus

So the foundation of this problem is the absence of consequence, and my answer to it goes back to the credit policy I mentioned earlier. At a minimum, the country needs to enforce that lenders must report to credit bureaus; Transunion, Creditinfo, Xdr, Experian, CRC, FirstCentral and the likes. This is the first step, and what it does is bring us to a level where a person can default only once before it holds them back. 

Today people take money from one lender after another, knowing that even if they fail to pay the current one, somebody else will hand them cash next month. That bad behavior sits at the bottom of the whole thing, and every lender who extends credit into it pays for the ones who came before.

If we manage to put reporting in place and build a credit-driven society, whatever default remains will be left to inability, and I have good reason to think this portion is smaller than most people expect. Lenders can seem very high-handed, yet most of them are able to restructure loans, and many borrowers with a genuine repayment struggle never go to their lender at all. 

When people do speak to lenders about their situation, restructuring happens, which tells me a good share of the inability problem can be handled with a conversation that many borrowers avoid. There is also a second effect worth mentioning; someone who has taken a loan before and has not paid will have that history sitting with the credit bureau, and they will struggle to get a loan somewhere else. That means many of these borrowers would never end up holding a pile of loans they cannot pay back in the first place.

Consumer loan default in Nigeria, and in Africa generally, comes from a combination of unwillingness and inability, and I can say this with confidence after everything I have seen. Inability exists and deserves a proper response from lenders, yes. But unwillingness is so pervasive that it makes up the bigger problem.

If I had a dollar for every person I’ve watched sign a loan contract without reading a word of it, I would be a retired billionaire with one of those super yachts in Monaco or just lounging in one of my many old Ikoyi homes. Unfortunately for me and me alone, this discovery has never translated to money. 

And while you may think, this old man is only trying to exaggerate, I promise you that’s not the case at all. This is the ordinary condition of consumer lending almost everywhere, and it gets worse the further down the income ladder you go. People rushing to solve a problem, whether it is school fees due next week or a business that needs inventory before the market opens, will skip past the fine print because the fine print feels like an obstacle between them and the money they need right now. 

And the fine print, as anyone who has ever been burned by a contract knows, is where all the consequences live. A misplaced comma, a clause about compounding interest, a line about what happens after day 30, these are the things that decide whether someone comes out of a loan intact or comes out of it owing three times what they borrowed. Sometimes including their kidneys.

There is a version of this problem where you could call it negligence. I mean, someone with two eyeballs, a university degree, a stable salary, and the time to read a contract who chooses not to read it has made a choice for sure, and there is an argument that the consequences of that choice belong to them. But that argument begins to fall apart the moment you look honestly at who truly needs consumer credit in an emerging market like ours. 

Across Africa and most emerging markets, credit invisibility tracks poverty almost perfectly, and poverty tracks limited access to education almost as closely. The people who need a loan the most are frequently the people least equipped to interrogate the terms of that loan. They are not being negligent when they sign without understanding. Oftentimes, no one sat them down and explained what they were about to sign before they signed it.

Nobody signs up to be exploited

Now here’s the hard-to-sit-with part of this conversation we shy away from. If you refuse to lend to people who might not fully grasp the terms, and you’ve decided that access to credit is a privilege reserved for the literate and the comfortable, which defeats the entire point of financial inclusion. But if you take the opposite position and treat every borrower as a fully informed adult who understands what they signed, and you’re pretending that even educated people don’t get tripped up by loan terms, then you’re being dishonest.

Most borrowers have no idea some lenders capitalize unpaid interest, folding it back into the principal so the balance grows even as they’re making payments. And in certain cases, some lenders build in punitive penalties that only reveal themselves after a missed payment. A few “good” lenders build in restructuring options that could genuinely help a borrower in trouble, but since nobody reads the contract in the first place, those options sit unused while the borrower panics and disappears instead.

Sadly, the people this hits hardest do not know what happens after a missed payment, nor do they expect to be reported to a credit bureau, and they are often blindsided when a collections call arrives, because in their mind, being broke or sick or unable to pay is a good enough reason. Some of them borrowed for reasons that had nothing to do with cash flow planning in the first place, a wedding or as the Yorubas would say owanbe, a burial, an obligation that culture and family pressure made non negotiable, and only realized afterward that they had no plan for repayment. 

A non-negotiable ethic the industry must uphold

Before getting into what responsible lending should look like in practice, I want to be straightforward about the part of this that is not gray at all. While most ethical questions come with shades of complexity, deliberately hiding terms from people who cannot protect themselves, or structuring a loan so that a borrower has no real path to walk away once they understand what they signed, is deeply wrong. 

There is no framing of business necessity or market competitiveness that makes it acceptable to trap someone who did not have the means to see the trap coming. Lending to people you know cannot meaningfully consent to your terms, and doing it because their vulnerability makes them profitable, deserves to be called exactly what it is.

I say this with the weight of something I watched happen firsthand. Years ago, while I sat on the board of one of the larger fintechs operating across Nigeria and the wider African market, one of our own employees, a young man in his early twenties working inside one of the better fintechs on the continent, took his own life after taking out a loan he could not repay and finding himself under pressure he could not withstand. 

I have not forgotten it, and I do not expect to. If someone working inside the industry, with more visibility into how lending works than the average borrower will ever have, can be pushed to that point, it tells you something about how much damage an unmanageable loan can do to a person who has far less protection than he did.

Tala went through something similar in Kenya, where several of their customers died by suicide tied to loan pressure, and I want to be careful here, because Tala is not a company I would call predatory or reckless. Plenty of what they built was genuinely good for financial inclusion. But even well-intentioned lenders can create conditions that break people, and that should sit with every founder and executive in this industry as a reason to build differently, not as a scandal to distance yourself from. 

What does responsible lending look like for the average lender?

Regulators like Nigeria’s FCCPC, and similar bodies across Africa, are already pushing towards a more responsible lending terrain with tough enforcement, and I think they are right to. Loan terms should be plain enough that nobody needs a law degree to understand them.

There is room for sophisticated, layered products aimed at middle class or highly educated borrowers who want that complexity, but for everyone else, the terms should be stated in the most direct language possible. Borrow $100, repay $120 over six months at $20 a month, full stop, with every fee disclosed upfront. If there is a penalty for missed payments, it should be disclosed clearly and it should be capped, so a borrower who falls behind is not staring at a debt that has doubled in size. 

Communication matters just as much as the terms themselves. A borrower should hear from their lender before a payment is due, not on the day it is due and certainly not after. For a loan repaid monthly, a reminder a week ahead gives someone time to plan. For a loan repaid weekly, three days ahead does the same job on a shorter cycle. 

When a payment is missed, the lender owes the borrower ongoing, human communication before escalating to a credit bureau report or anything more severe, and that communication should always include an open door to restructuring, offered without an additional fee attached, because charging someone to fix a problem you could have helped them avoid defeats the purpose of offering the option at all.

Interest capitalization deserves particular scrutiny when the borrower is vulnerable. Letting unpaid interest fold back into the principal can make a loan balloon out of proportion to what someone can reasonably work their way out of, and once a borrower reaches that point, frustration turns into resignation, and resignation is where the worst outcomes come from. 

How you and I can uphold responsible lending

A lot of what I have described so far comes down to something far less complicated than it sounds. When a lender suspects a borrower is under pressure, or worries that person may not fully grasp what they are agreeing to, the answer is as basic as a phone call. 

Ask the borrower directly whether they understand the loan and whether they have a plan for what the money will actually be used for, and let their answer guide what happens next. Where it is possible to hand over goods instead of cash, inventory for someone running a small business, tuition paid straight to a school rather than routed through a parent’s account, lenders should take that path every time, because it closes off an entire category of misuse and diversion that cash lending leaves wide open for vulnerable borrowers.

None of this asks lenders to reinvent how they operate, they’re merely choosing transparency over obscurity and patience over aggression, and both of those are choices sitting well within their control. A lender who will not build in these protections has decided, whether they say it out loud or not, to profit from a borrower’s confusion, and there is no gentle way to describe what a business built on that foundation deserves.

The poor and vulnerable are the people fairness and consent matter most to, yet they have the least power to demand either. And it’s worth saying outright that being a brokeass isn’t a license for anyone to take advantage of you. Unfortunately, history is full of exactly that. Wherever there’s a gap in power or information, someone finds a way to profit from the side that can’t push back.

Sadly, in most parts of the world especially in third world countries, poverty and access to credit are like siamese twins, eternally tangled up together in ways that make life a living hell. A large share of the world’s poor stay poor partly because the one thing that could pull them out, formal credit, stays locked behind a door they can’t open.

So hand them credit, then. Problem solved? Not so fast. Most of them show up to that “door” with lean credit history or none at all, no track record for a lender to judge them by, and this gap alone can be enough to seal their fate. 

On the contrary, if we take a look at more advanced nations, say the US for example, FICO built the gold standard for scoring, and while it doesn’t get things right every time, it works often enough that an entire financial system was built on top of it. What FICO does well is synthesise a huge volume of structured data into a single, defensible number, pulling from schemas refined over decades, with fields that map to risk in ways banks have trusted for generations. 

Such infrastructure doesn’t exist across most of Africa, Latin America, and Southeast Asia. It also hardly exists for a new immigrant building a life in a Western country with no local credit history to their name. The data required to make a sound lending decision is either thin, scattered across systems that don’t talk to each other, or simply absent. This is the exact problem I’ve spent years thinking about at Lendsqr. That is, until AI-driven consumer credit scoring models became mainstream.

Where AI fits into the story 

AI’s role here is a specific answer to a specific data problem. To see why,  it helps to pull apart two words people use interchangeably: alternative data and unstructured data. They are not the same thing, and confusing them is where a lot of the AI-and-lending conversation goes wrong before it starts.

Alternative data is any data source outside the traditional credit-reporting menu. Mobile-money transaction histories, airtime top-up patterns, utility-payment records all qualify. Plenty of it still arrives in rows and columns, the same shape as a bank statement, just pulled from a source a credit bureau never touched. A model can read six months of mobile-money inflows and outflows almost the way it reads a repayment history, because the underlying structure isn’t so different.

Unstructured data sits on a separate axis entirely. SMS messages, call logs, voice recordings, photos, a free-form conversation with a loan officer or a chatbot, none of it arrives ready to plug into a scorecard. Something has to read it, listen to it, or parse it first and turn it into a signal a model can use, and that’s where AI earns its keep. A sheet of utility payments could, in principle, be scored by a decades-old statistical method. A folder of someone’s SMS inbox could not, not without a system built to make sense of language at scale.

Data can be alternative and structured at once, like the mobile-money example. It can also be alternative and unstructured at once, like the SMS example. Two different labels, with two different sets of tools required, and treating them as one blur called “AI risk” is how the conversation gets lost.

Fairness is about what the model does with what it sees

Fairness, here, has nothing to do with intent. Nobody at Lendsqr sits down to build a model that penalizes people for their race, gender, or location. Unfortunately, bias moves in more roundabout ways. Say a model discovers that people who let their prepaid airtime balance run down to almost nothing before topping up are more likely to default. Fine, the pattern holds up inside the data. 

But what is it really capturing? It could be income volatility or could be a proxy for a region or income bracket where that habit is simply how people manage scarce cash. The model doesn’t know the difference, and left unchecked it will punish an entire group for a behaviour that has more to do with their circumstances than their creditworthiness.

That’s the difficult question, and it isn’t “does AI have bias.” Every model, human or machine, carries some. The difficult question is whether a model can stay genuinely useful and predictive while still producing outcomes we’re willing to call fair, and that has to be asked variable by variable.

Consent is about what a lender is entitled to learn

Where fairness asks what a model does with information someone already handed over, consent is more of what a lender is entitled to learn from that information in the first place, and AI makes the question uncomfortable in a way older systems never had to take into account.

Take for instance a borrower who agrees to let a lender read their SMS inbox to verify transaction alerts, believing the request is that narrow. If an AI model, built to extract every available signal, is run through the same inbox, income stability can be revealed, other loans the borrower is juggling, spending habits, signs of financial distress, even relationships they never meant to disclose. None of that was on the form they signed, but it sat inside data they’d already agreed to share, waiting for a system capable enough to find it.

So the question stops being “did I consent to you accessing this data.” It becomes “did I also consent to everything you’re capable of figuring out from it.” Those are two different permissions, and most consent flows, ours included, weren’t built with that distinction in mind.

Now for the side of this that gets less airtime. If fairness and consent risk make us uneasy enough to pull back hard on AI and alternative-data scoring, people with thin or non-existent credit history don’t land softly in some cleaner, fairer traditional system. I’ll be honest with you, there is no such system waiting for them. 

A little history on how we got to the AI frontier

The questions around fairness, consent, and who gets left out didn’t begin with today’s credit scoring systems. Every generation has had its own version of this fight. But, to understand where we are now, it helps to look at how we got here.

In 1956, that’s when modern credit scoring began, when engineer Bill Fair and mathematician Earl Isaac built a system on the idea that subjective lending decisions needed to give way to statistical analysis. For years the formula stayed a black box the customer never saw. That began to change in 1974, when the United States adopted the Equal Credit Opportunity Act, prohibiting lenders from denying credit on the basis of race, religion, national origin, sex, marital status, or age. It put scoring systems on notice that however technical they looked on paper, someone still had to answer for the outcomes they produced.

A more consequential shift, for the purpose of this article, was happening far from Wall Street. In 2012 the Commercial Bank of Africa and Safaricom launched M-Shwari in Kenya, giving people with mobile-money accounts but no formal credit history access to loans scored on their transaction and airtime behaviour, reaching millions of first-time borrowers within its first year. 

By 2014 a wave of app-based lenders, Tala (then still called Mkopo Rahisi) and Branch among them, had entered the market, pushing further into unstructured signals like phone metadata and app usage to drive underwriting. This is the tradition Lendsqr sits inside, and it’s where the fairness and consent questions in this article stopped being theoretical for a lot of people who had never had a credit history.

Regulators caught up rather quickly even if not consistently. The EU’s GDPR, in force from 2018, gave consumers new rights over automated decisions made about them and pushed the question of whether an AI model owes a person an explanation into mainstream law. 

In the US, the CFPB confirmed in 2022 that federal anti-discrimination law requires lenders to explain the specific reasons behind a denial, even when relying on complex algorithms, and followed up in 2023 to close the loophole of generic, checklist-style reasons that didn’t reflect why a model made its decision. Using a more powerful model doesn’t buy a lender the right to a vaguer answer. AI is best read as the latest chapter in that history, arguably its most capable one.

So where does that leave us

This debate, unfortunately, doesn’t resolve easily, and I’ve made my peace with that. This piece has sat inside several tensions: alternative data against unstructured data, fairness against consent, exclusion against imperfection. Each one comes down to the same discipline: staying honest about what a model can see, what it can infer, and who could get hurt because of it.

The risk will never completely disappear and I don’t think pretending otherwise helps anybody.

Planes are not perfectly safe either because people still die in plane crashes. Yet nobody seriously argues that the answer is to stop flying. Instead, we continue to make planes safer, again, and again, and again. Today, based on the last five years of commercial aviation data, roughly 99.99998% of flights do not end in a fatal accident. The remaining risk is still real, and every accident still matters, but the answer has been relentless improvement rather than abandoning flight.

By the way, nobody is going to drag me on a helicopter – the risk of that is simply unacceptable to me.

I think AI-driven credit scoring deserves the same treatment.

For millions of people without a conventional credit history, there isn’t a pristine scoring system waiting in the wings if we decide AI makes us uncomfortable. In many cases, there is simply no score, no credit, and no opportunity. So the sensible response cannot be to demand that AI carry zero risk before we’re willing to use it. It should be to build guardrails that keep pushing that risk down.

Explainability is one of them.

If an AI system recommends declining someone, “the model said no” should never be an acceptable answer. A lender should be able to see something closer to: based on signals X, Y, and Z, this borrower has an estimated B% probability of default, compared with K% for comparable borrowers. The reasoning should be visible enough to interrogate, challenge, and, where appropriate, override.

And humans should remain capable of overriding it.

That matters because a score is ultimately a prediction, not a commandment. At Lendsqr, this is already part of how we think about responsible and ethical lending: technology should make the lender better informed, not remove judgment or accountability from the lender.

There should be other guardrails too. Regulators can require explainability. They can require lenders to audit models for unfair outcomes. They can draw a hard line between using data to understand repayment risk and using someone’s vulnerability simply to extract more from them. And as these systems become more capable, those rules will have to become more capable alongside them.

None of this will make AI perfectly fair. None of it will make every inference comfortable. And none of it guarantees that nobody will ever be harmed by a lending decision.

But perfection is the wrong destination to drive to. What should matter is whether we can make these systems safe, explainable, accountable, and fair enough that the enormous benefit of giving previously invisible borrowers a genuine shot at credit outweighs the risks that remain.

I believe we can.

So I will keep building but also stay willing to be told where we’re wrong, because that is how you make the next version safer than the last one.

Ó dàbọ̀.