A trader in Kano has never had a bank loan. She has never owned a credit card. She has no formal employment record, no payslip, no collateral to offer. By every traditional lending metric, she does not exist as a creditworthy individual.
But she has had the same mobile money line for four years. She tops up airtime every Friday without fail. She receives and sends money through her phone dozens of times a month, in patterns that are remarkably consistent. She pays her electricity token regularly. None of this looks like a credit file. All of it is one.
This is the insight that is quietly reshaping lending across Africa — and unlocking credit for an estimated 300 million people who have never had it. Traditional credit scoring asked: what is your formal financial history? Alternative credit scoring asks a different question entirely: what does your digital life reveal about your reliability? The answer is transforming who can borrow money on the continent, and it is happening faster than most people realise.
“M-KOPA has unlocked over $1.5 billion in credit, impacting over 23 million people almost none of whom had a formal credit history before.” — ICECCME Research, 2024
57%
Africa’s unbanked adult population roughly 350 million people without formal bank accounts
90%
Prediction accuracy achieved using mobile data credit models vs under 80% for traditional methods
40%
Increase in loan approval rates enabled by P2P and alternative lending platforms
$1.5B
Credit unlocked by M-KOPA alone, reaching over 23 million people across Africa
Why Traditional Credit Scoring Fails Most Africans
To understand why alternative credit scoring matters so much in Africa, you need to understand exactly how badly traditional credit infrastructure has failed the continent.
Conventional credit scoring relies on a small number of formal data points: bank account history, employment records, existing loan repayment behaviour, and collateral. This system works reasonably well in markets where most adults have bank accounts, salaried jobs, and documented assets. It works terribly in markets where the majority of economic activity happens informally.
In Nigeria specifically, roughly 26 percent of the adult population about 28.8 million people have no credit history whatsoever, making it structurally impossible for banks to extend them credit through conventional means. Across the continent, an estimated 57 percent of adults remain unbanked, locking hundreds of millions of people out of formal credit not because they are not creditworthy, but because the system was never built to assess people who work as market traders, artisans, smallholder farmers, or gig workers.
This is not a small market failure. It is one of the largest in the global economy. Small businesses across Africa cite access to finance as their single biggest constraint to growth, more often than power, more often than infrastructure, more often than regulation. The credit gap for African SMEs alone is estimated in the hundreds of billions of dollars annually. Every business that cannot access working capital to buy inventory, every farmer who cannot finance the seeds for the next planting season, every trader who cannot smooth cash flow between supplier payment and customer sale, represents lost economic growth that better credit infrastructure could unlock.
How Alternative Credit Scoring Actually Works
The technical innovation underlying Africa’s lending revolution is the use of non-traditional data sources to build a picture of creditworthiness that does not depend on formal financial history.
Mobile network operator data is the foundation. Call frequency, data usage patterns, airtime top-up consistency, and mobile money transaction history all turn out to be remarkably predictive of repayment behaviour. A 2024 academic study presented at the International Conference on Electrical, Computer, Communications and Mechatronics Engineering tested a logistic regression model using exactly this kind of data — call frequency, data usage, and mobile money transactions and achieved 90 percent prediction accuracy for creditworthiness, compared to under 80 percent using traditional credit methods alone. In a market where traditional methods cannot even be applied to most of the population, a 90 percent accurate alternative model is not an incremental improvement. It is the difference between credit access and credit exclusion.
Beyond mobile data, the alternative scoring toolkit has expanded significantly. Utility bill payment history, electricity tokens, water bills reveals consistency in meeting financial obligations. E-commerce activity, where available, shows purchasing patterns and order fulfilment reliability. Social media activity, while more controversial from a privacy perspective, is being used by some lenders to verify identity and assess network stability. GPS and location data can verify business activity claims confirming, for instance, that a loan applicant claiming to run a shop at a specific market location is actually present there regularly.
RiskSeal, a credit data company operating across Nigeria and other African markets, now provides lenders with over 400 distinct alternative data points per applicant — digital footprint scores, fraud risk indicators, solvency metrics, and identity verification signals. This level of granularity allows lenders to make underwriting decisions on borrowers who would have been completely invisible to a traditional credit bureau just five years ago.
“Mobile money transactions, airtime purchases, and bill payments are being used to build credit profiles. A 2024 academic model using only mobile data achieved 90% prediction accuracy outperforming traditional credit scoring methods.”
The Business Models Built on Alternative Data
Several distinct business models have emerged across Africa, each using alternative credit data in a slightly different way.
1. Asset financing with embedded credit scoring
M-KOPA’s pay-as-you-go model, covered in detail in our previous profile, is the clearest example of alternative credit scoring at massive scale. Every daily payment a customer makes for a solar panel or smartphone builds a behavioural credit record. M-KOPA has used this approach to unlock over $1.5 billion in credit, reaching more than 23 million people across Kenya, Uganda, Nigeria, Ghana, and South Africa, the overwhelming majority of whom had no formal credit history before their first M-KOPA product.
The model works because the asset itself functions as both the product and the credit instrument. A customer is not applying for an abstract loan; they are financing a specific tool, with the device’s remote-lock capability serving as the security mechanism that traditional collateral would normally provide.
2. SME lending platforms
Nigeria is home to over 250 fintech companies, and roughly 15 percent of them are now specifically focused on SME lending, a dramatic shift from the early Nigerian fintech wave, which was dominated by payments. These platforms use a combination of transaction history (often from the same payment rails the fintech operates), business registration data, and increasingly, point-of-sale transaction data from merchants who use the lender’s payment terminals.
This creates a powerful flywheel: a fintech that processes a merchant’s daily sales transactions has, embedded in that data, a real-time picture of the business’s cash flow, seasonality, and growth trajectory, a far richer credit signal than any static loan application could provide. Moniepoint and similar Nigerian platforms have built exactly this kind of embedded lending capability on top of their payment processing infrastructure.
3. Peer-to-peer and community-based digital lending
Traditional African community savings structures chamas in Kenya, ajo or esusu in Nigeria, tontines across Francophone Africa have existed for generations, built on social trust and group accountability rather than formal credit scoring. Digital platforms are now formalising these structures, layering technology onto community-based trust networks that already had strong repayment cultures.
Academic research published in 2024 found that peer-to-peer lending platforms built on these community models have expanded loan approval rates by 40 percent in markets where they have been deployed demonstrating that digitising an existing trust-based system can be even more effective than building an entirely new credit assessment framework from scratch.
4. Buy-now-pay-later and merchant credit
E-commerce and retail-adjacent lending financing a purchase at the point of sale rather than providing cash credit is one of the fastest-growing categories. South Africa’s alternative lending market alone grew 29.5 percent in 2024 to reach $297.2 million, with major players including digital-native lenders like Lulalend, alongside established banks like Nedbank and African Bank acquiring fintech capabilities to compete. The sector is projected to reach $619.6 million by 2028, a compound annual growth rate above 20 percent.
The Risks Nobody Should Ignore
Alternative credit scoring is not without serious risks, and any honest assessment of the sector needs to address them directly.
Privacy is the most pressing concern. Using mobile phone usage, social media activity, and location data to assess creditworthiness raises significant data protection questions, particularly in markets where data protection regulation and enforcement capacity are still developing. Borrowers frequently do not fully understand what data is being collected or how it is being used to make lending decisions about them.
Algorithmic bias is a related risk. Models trained on historical data can encode and perpetuate existing inequalities. For instance, systematically scoring rural borrowers lower than urban ones, or disadvantaging borrowers in regions with weaker mobile network infrastructure, regardless of their actual creditworthiness. Without careful auditing, alternative credit models can replicate the exclusion they are meant to solve, just through a different mechanism.
Predatory lending practices have also emerged in parts of the digital lending ecosystem, particularly among smaller, less regulated mobile lending apps that combine alternative scoring with aggressive collection practices, hidden fees, and short repayment windows that trap vulnerable borrowers in cycles of debt. Kenya’s Competition Authority identified several hundred digital lenders operating in the market before the COVID-19 pandemic, and regulatory oversight of this fragmented landscape has struggled to keep pace with the sector’s growth.
What This Means for the Next Five Years
The trajectory is clear: alternative credit scoring is moving from a niche innovation to the dominant model for consumer and SME lending across Africa. As more economic activity becomes digital, more transactions happen through mobile money, more commerce moves online, more utility payments are made digitally, the volume and richness of alternative data available to lenders will keep expanding.
The most significant structural shift to watch is the move from single-source to multi-source scoring. The most sophisticated lenders are no longer relying solely on mobile network data, or solely on e-commerce activity, or solely on community lending history. They are combining multiple data streams, RiskSeal’s 400-plus data points being one example to build composite credit profiles that are both more accurate and more resistant to manipulation or fraud than any single data source could provide alone.
For the 300 million-plus Africans who remain functionally invisible to traditional credit bureaus, this is not an incremental improvement. It is the difference between a continent where credit access depends on having the right paperwork, and one where it depends on demonstrating reliability through the digital traces of everyday life. That shift, quiet, technical, and unglamorous as it may sound is one of the most consequential financial inclusion stories happening anywhere in the world right now.
