A woman running a profitable trading business in Lagos or Addis Ababa can get
her first loan today in ways that were impossible a decade ago. Alternative data
and AI-enabled scoring are helping lenders identify creditworthy women-owned
businesses that traditional crediting overlooks entirely. While that’s a leap, it
doesn’t go far enough. Current evidence proves access but not scale and
progression: these mechanisms work well for micro-lending and mostly stop
working at the point at which a business is ready to scale.
The information gap
Across African markets, women-owned businesses are overlooked by financial institutions for
multiple reasons, including products, processes and operating models that are not designed around how many women entrepreneurs run and grow their businesses. One of the most persistent barriers is perceived creditworthiness. Lenders often lack the information they traditionally rely on to assess these businesses. Women are less likely to hold titled property or other assets accepted as collateral, and are more likely to run informal, cash-based businesses that leave little structured credit history.
IFC data across 183 client financial institutions from 2024 highlighted that women-owned SME loans made up just 19% of SME portfolio volume and were 28% smaller on average. Despite these differences, their longer-term repayment performance was comparable to the overall portfolio, with 90-day NPL rates of 3.6% and 3.8%, respectively. This points to a gap between how women-owned businesses are assessed and the risk they ultimately demonstrate: they receive smaller loans, despite comparable repayment performance. That information gap is precisely what makes alternative data worth taking seriously.
What alternative data unlocks
By drawing on mobile-money histories, cash flows, group-savings records and guarantee
mechanisms, lenders can price risk for borrowers that conventional crediting simply cannot see. In Nigeria, a digital cash flow lending product built with Access Bank and Sterling Bank, and backed by the World Bank’s Women Entrepreneurs Finance Initiative (We-Fi), replaced collateral requirements with business transaction data. Following promising initial results, the model is now being adapted for Ghana and Sierra Leone, including through measures such as biometric identity verification to accelerate KYC and alternative credit scoring.
AI can make alternative-data scoring faster and easier to apply at scale. Machine-learning models can analyze a wider range of information, including mobile-money use, sales, savings and repayment histories, and automate parts of the credit-assessment process for borrowers with limited conventional credit histories. Where the necessary data is already available and accessible, this can also reduce the cost of assessing large numbers of small-ticket borrowers.
But AI and models can only be as good as the data they are built from. This can create problems in two ways. First, if low-income women or women-led businesses are largely absent from the data pool, the model cannot assess them at all: only 31% of women in Africa used the internet in 2024, against 43% of men, reflecting a broader digital-access gap that limits the data available on women borrowers. Second, even when women-led businesses are present in the data, a model trained mostly on larger or male-led businesses can embed assumptions that do not reflect how women actually run their businesses, biasing scores against them. A related issue is that women remain underrepresented in building and governing these systems, which can further embed assumptions misaligned with women’s business realities. Without deliberate correction, AI-enabled scoring can reinforce the bias entrenched in traditional scoring methods, unless an intentional lens to address bias is brought in.
Where the ceiling sits
Alternative data has a ceiling, and it sits right where growth begins. The models mentioned above
work well for small, transaction-based lending, where automated credit-scoring systems can assess a mobile-money-backed inventory loan and enable approval within minutes. They were not built for the loan a woman needs to buy machinery for her business or to formalize a growing operation. Larger, asset-based financing reintroduces the collateral and cash flow assessment that alternative data was meant to replace.
But the ceiling is not only technical. Even where lenders could extend alternative-data approaches or blend them with traditional underwriting for larger loans, women reached through entry-level products are not always treated as a segment with an intentional pathway into larger financing. There is relatively little evidence tracking whether successful borrowers go on to grow their businesses, absorb larger loans and transition onto more commercial products. That creates a reinforcing gap: without evidence of progression, lenders have less basis to design for it.
That gap reflects Africa’s broader “missing middle”: one Ethiopian program built for growth-oriented women entrepreneurs averaged loans of around $12,000 each once borrowers moved beyond group lending, a size too big for microfinance and too small for commercial banks.
Pioneering firms are translating access into progression
A small number of financial institutions and fintechs are beginning to go beyond first-time access by tracking what happens after an initial loan and, in some cases, creating pathways into repeat or larger financial products. Their experience offers early evidence that alternative-data lending can support progression, not just entry into formal credit.
Kifiya, the Ethiopian fintech behind the AI-enabled credit infrastructure for several partner banks, was featured by the World Bank Group and IFC in a 2026 case study as a model for alternative-data lending at scale, having enabled more than $755 million in credit and insurance and scored over 4 million loan applications. Its own post-loan tracking is the more useful data point here: in a survey of 1,792 borrowers, 99% of them women, 88% reported their business was still active after the loan and 78% reported increased income. Portfolio-wide, 65% of Kifiya’s borrowers over the past year were repeat borrowers – a figure worth holding.
In Egypt, MNT-Halan’s AI-powered scoring engine, built on transaction and behavioral data, has automated more than half of loan approvals and pushed approval rates for previously unscorable borrowers to 60%, with the company pointing to individual customers who moved from a first credit limit to a credit card to investment products. That shows real progress, but it is self-reported by the company rather than independently measured, which is exactly the gap the sector still needs to close.
Access Bank’s cash flow product is also the closest thing to controlled progression evidence in this piece. Between January 2021 and August 2023, the bank disbursed more than 20,000 cash flow loans to over 15,500 business customers—30% of them women—at a 99% repayment rate, with 45% of borrowers returning for a repeat loan.
Taken together, these cases are promising, but the evidence base remains early. Much of the available progression data is still generated by providers themselves, and relatively few programs
systematically track whether borrowers move from initial access into larger loans, commercial terms, and sustained business growth. Building that evidence base, and sharing what works across institutions and markets, will be critical if alternative-data lending is to become a pathway to growth rather than simply another entry-level product.
What this means for institutions
For African financial institutions, the pressure-point is whether they can build and measure the whole customer journey: tracking loan-size growth and transition to commercial terms past the first approval, rather than treating a first disbursement as success. That also means testing any model across both banks and fintechs, and across markets. What remains unproven is whether these models can consistently help women-owned MSMEs move from initial access to larger, more commercial financing.
