Lending in Africa is built on a very different foundation than what exists in the West. In places like the United States or Europe, lenders have the advantage of long-established credit bureaus, decades of consumer data, and the predictability of steady, formal employment. With that infrastructure, risk models are relatively straightforward. Pull a credit report, check a salary history, and project repayment behavior with a fair degree of confidence.
In Africa, the picture is not nearly as neat. Millions of people are completely outside the reach of formal banking. They earn and spend in ways that never leave a trace in the kind of records Western systems depend on. Even those who have some interaction with banks often don’t have the kind of consistent financial trail that would feed into a traditional score. Add to that the frequent swings in inflation, volatile exchange rates, and the different cultural outlooks on debt. You start to see why simply importing Western credit models into African markets does not hold up in practice. The models either exclude too many people or misjudge risk entirely.
That doesn’t mean African lenders are working in a vacuum. Far from it. What it means is that lenders must shape their risk models around how people live and transact across the continent. No framework built for credit cards and mortgage histories can fully explain an economy where mobile money wallets outnumber bank accounts, borrowers depend on seasonal harvests or informal trading, and people often trust community-based lending systems more than formal banks. To make lending sustainable and fair, African lenders need models that recognize these realities instead of trying to fit them into assumptions that belong elsewhere.
Data gaps Western models can’t handle
Across much of Africa, the kind of banking data that lenders in the West take for granted is often missing or incomplete. The World Bank reported in 2021 that less than half of adults in sub-Saharan Africa had a bank account. In some countries, the situation is even starker. South Sudan, for instance, recorded only about 6% of adults with accounts in 2022. Compare that with countries like the United States, where over 90% of adults use banks regularly, and the difference is hard to ignore.
This absence of formal banking data creates a fundamental problem for credit models built in Western markets. Those systems are designed to crunch years of repayment histories, credit card bills, mortgage records, and other financial trails. In Africa, a huge portion of borrowers have never had access to those products. Someone applying for a loan may be running a successful small business or reliably supporting a household, yet still have no bank statement or credit bureau record to prove it.
Nigeria illustrates this gap clearly. Banks there have acknowledged that the lack of credit history is one of the main reasons potentially qualified borrowers get turned away. In effect, the absence of data becomes a barrier just as strong as poor credit performance would be in a Western context. For most first-time borrowers in Africa, there simply isn’t a paper trail for a traditional model to analyze.
Faced with this reality, lenders on the continent have had to expand their definition of what counts as reliable information. Instead of looking only at formal banking interactions, they are finding signals in other parts of daily life. Mobile phone usage, utility bill payments, rent contributions, and even participation in community savings groups all begin to take on weight. These are not areas Western lenders typically build into their models, but in Africa they often provide the clearest and most consistent picture of whether someone can handle credit responsibly.
Also read: A lender’s guide to understanding risk assessment
A borrower’s informal and community-driven world
For many Africans, income does not come from a paycheck that lands neatly in a bank account every month. It comes from trading in open-air markets, small-scale farming, driving a bus or motorcycle taxi, or working as an artisan. These are productive activities that keep households running and businesses alive, but they do not produce the kind of documents Western lenders expect. There are no payslips, no steady tax filings, and often no official bank statements that tell a lender how much the person earns or spends.
Instead, money tends to move through informal systems that operate on trust and social accountability. Across the continent, rotating savings and credit associations are widespread. They go by different names depending on the country (esusu or ajo in Nigeria, stokvel in South Africa, chama in Kenya), but the principle is the same. A group of people pool contributions, and each member takes turns accessing the funds. These groups operate without regulation, yet people who use them often find them more dependable than formal systems.
Regular participation in such groups is, in practice, proof of reliability. Someone who consistently contributes to a savings pool, week after week, is demonstrating both financial discipline and commitment to their community. The problem is that traditional credit scoring models built in the West do not see this behavior at all. What looks like strong evidence of trustworthiness in an African context becomes invisible to a system designed for credit cards, mortgages, and car loans.
Another important difference is the way borrowers view debt itself. In much of Africa, people tend to approach borrowing with caution. They usually take on debt for a specific purpose, such as paying school fees, financing a harvest, or expanding a small business. Borrowers often try to clear these obligations as quickly as possible. In contrast, people in Europe or the United States carry credit card and installment loan balances for years because these forms of credit are deeply ingrained in everyday spending. African borrowers, on the other hand, tend to treat debt as a temporary necessity.
These differences shape repayment behavior in ways that Western models cannot capture. A scoring system built on the assumption that borrowers will regularly revolve credit card debt has little relevance in an economy where people borrow sparingly, repay in lump sums, and rely heavily on community-based systems. For African lenders, understanding this informal and community-driven world is not optional. It is the starting point for building risk models that actually reflect how people live and manage their money.
Mobile phones and alternative data are rewriting the rules
One of the most overlooked realities about Africa is that while banks struggle to reach millions of people, mobile phones already sit in their pockets. For lenders, that single fact changes the entire equation. A phone is no longer relegated as only a communication tool, but has become the financial diary of millions of people.
Every airtime top-up, every electricity bill paid through mobile money, every remittance sent to a sibling tells a story about how someone earns, spends, and manages their money. For a Western lender, this may look like noise. For African lenders, it is often the cleanest and most reliable signal they have.
That is why fintech lenders like Tala and Branch lean heavily on these trails of activity. A borrower who consistently loads the same amount of airtime or never misses a utility payment is showing a rhythm of responsibility that a payslip or tax return might never capture. Even telecom giants like Safaricom in Kenya or MTN across West Africa have stepped into this space, sharing anonymized insights that give lenders a sharper view of who can be trusted with credit.
Also read: Frequently Asked Questions about Lendsqr
Regulation and incomplete infrastructure
Credit reporting is still catching up in many African countries. While progress is being made, it is uneven. Nigeria, for instance, has introduced open banking rules to make data sharing easier, but the reality on the ground is that coverage is still thin. A large share of borrowers do not show up in the credit bureaus at all, so lenders are often left with very little to cross-check when deciding on an application.
Government policies also shape the lending environment in very particular ways. Interest rate caps are one example, sometimes set with the goal of protecting borrowers but often leaving lenders struggling to balance costs. At the same time, governments are experimenting with tools to encourage more credit flow. Nigeria’s National Credit Guarantee Company provides partial guarantees on loans to small businesses, which can give lenders some extra security. But those kinds of schemes are only one piece of the puzzle. To actually manage risk, lenders still have to rely on their own data, models, and internal processes to understand repayment behavior and make sound decisions.
What a better African risk model looks like
Given the gaps in infrastructure and regulation, African lenders have had to be more inventive in how they assess risk. The strongest models are those that combine data, context, and constant adaptation rather than trying to copy-paste what works in more developed markets. Some of the features that make a difference include:
Alternative and digital data at the core. In places where most people are still new to formal borrowing, traditional credit reports rarely tell the full story. Instead, lenders look at how people use mobile money, how regularly they top up airtime, or whether they keep up with paying utility bills. These details can paint a far clearer picture of day-to-day financial discipline than a bureau report that only covers a fraction of the population.
Context-specific behavioral signals. People earn and spend in very different ways across African markets, and a good model needs to reflect that reality. A bolt driver in Lagos will likely have unpredictable daily income, while a vegetable seller in Freetown might earn more steadily but at smaller amounts. Some fintechs have started building models around these patterns. These tweaks recognize the cadence of local livelihoods and make risk assessments more accurate.
Layered approaches. No single data source can capture the full risk profile of a borrower, so lenders often combine several methods. A borrower’s track record of repaying small digital loans might act as a first signal, while machine learning models then dig into broader usage patterns to refine the assessment. Some lenders even add short psychometric tests to capture things that financial data cannot reveal. When used together, these layers provide a more balanced and reliable view of creditworthiness.
Continuous updating. African economies shift quickly, which means risk models can become outdated within months if they remain static. Leading digital lenders avoid this problem by recalibrating regularly. In Kenya, for instance, M-Shwari has been reported to adjust its scoring models to reflect agricultural cycles, so that lending patterns line up with planting and harvest seasons. By updating their models every few weeks, these lenders stay aligned with the realities on the ground and avoid being caught out by sudden shifts.
Also read: How to track and reduce your loan portfolio’s delinquency rate
Building credit models that fit Africa
It is unrealistic for African lenders to simply pick up Western credit models and expect them to deliver the same results here. The sources of data are different, the way economies behave is different, and even how people think about and use credit follows its own logic. None of this should be seen as a disadvantage. In fact, the explosion of mobile money and digital finance across the continent has opened up an entirely new pool of alternative data that can be far more telling than traditional bureau records.
The real task for lenders is to shape models that make sense in this environment. After all, they are better protected against risk when the data sources show how people actually earn and spend.
At the same time, they also unlock the possibility of serving borrowers who would have been ignored in a purely Western-style system. That combination of stronger portfolios and wider financial access is what makes this moment so significant.
This is where technology comes in. Platforms like Lendsqr are already helping lenders put these kinds of models into practice, combining automation with flexibility so that credit decisions can reflect both scale and local realities. If done right, this shift will expand credit in a way that respects how African borrowers live and work.
