In Kenya, access to credit has long been limited by traditional lending systems that rely heavily on collateral, payslips, and established banking histories. For the nearly 9% of adults who remain unbanked or underbanked, these requirements have kept them excluded from formal financial services. But today, loan apps using AI and alternative credit scoring are reshaping the landscape of loan approvals in Kenya, offering faster access to credit for millions of people once locked out of the system.
The Shift from Traditional to Digital Credit Models
Historically, credit scoring in Kenya was dominated by bureau reports and banking records, tools ill-suited to informal workers, small-scale traders, and rural farmers. The result was a market where vast segments of the population were “credit-invisible.”
The rise of alternative credit scoring has changed that. By analyzing non-traditional data, such as mobile money transactions, airtime purchases, smartphone activity, and even GPS signals, AI-powered systems are creating new paths to financial inclusion. In a country where M-Pesa alone accounts for 50%–59% of GDP in transactions, mobile data has become the cornerstone of these new models.
How AI Powers Loan Approvals
AI algorithms process enormous datasets at high speed, allowing fintech lenders and banks to approve loans within minutes. For example, patterns such as consistent mobile bill payments, regular remittances, or steady savings habits can demonstrate reliability even if someone has no formal banking record.
Tala, one of Kenya’s leading digital lenders, uses more than 250 micro-indicators from smartphones, including app usage, text message frequency, and geolocation, to build borrower profiles. Branch International incorporates SMS logs and the diversity of a user’s contacts, while Safaricom’s M-Shwari adapts its models to farmers’ harvest cycles, ensuring loans are repaid when seasonal incomes peak. These innovations are helping reduce default rates to around 5%, similar to commercial banks but applied to borrowers considered riskier by traditional models.
Key Players Transforming the Market
Several institutions and fintechs have become major drivers of this transition:
- Tala and Branch: Together, they have disbursed over $600 million to more than three million customers across East Africa, most of them first-time borrowers without formal credit histories.
- M-Shwari: Launched in 2012 by Safaricom and NCBA Bank, it pioneered mobile lending in Kenya by combining call records, mobile money activity, and savings patterns. It now serves millions of customers nationwide.
- Patascore: A business-focused AI platform, it reduces manual underwriting by 80% and delivers credit decisions in under 30 minutes. It has already supported around 500,000 MSMEs in Kenya.
- TransUnion and FICO: In early 2025, they introduced a credit scoring system in Kenya that integrates 145 alternative data points, including utility and insurance records.
- IBM Research: The company has analyzed more than 10 million mobile phone records in Kenya to develop deeper insights into borrower behavior.
- Government initiatives: Kenya’s Hustler Fund uses mobile network data to extend instant loans to MSMEs without requiring collateral or formal banking records.
- MTN’s Qwikloan (powered by JUMO): Since 2015, it has disbursed over $8 billion in small loans to 31 million users across Africa, with Kenya among its major markets.
These platforms issue tens of thousands of small loans daily, often ranging from as little as $10 to $40, directly through mobile apps, with no paperwork required.
Transformative Impacts on Borrowers
The shift to AI-based lending has produced real-world benefits. A Nairobi fruit vendor can now access an instant loan to restock produce during peak hours, while rural farmers use seasonal loans to buy fertilizer and repay after harvest. Women entrepreneurs, youth, and immigrants, who are groups often excluded from traditional finance, are gaining access to working capital.
Harvard research on Tala found that $36 loans to first-time borrowers increased earnings by about 20%, reducing vulnerability to economic shocks. For lenders, automation has cut operating costs, accelerated decision-making, and made it possible to manage high loan volumes without raising default risks.
By 2025, these innovations have already made strides toward closing the $331 billion SME credit gap that existed in sub-Saharan Africa in 2018.
The Challenges of AI in Lending
Despite the benefits, challenges remain. Algorithmic bias is a risk, if models are trained on incomplete or skewed data, they may unintentionally favor urban men over rural women or minority groups. Transparency is another issue: borrowers often receive vague rejection notices with little explanation, eroding trust in the system.
Privacy is also a growing concern. With AI-driven lenders analyzing SMS records, call logs, and location data, questions of consent and surveillance are increasingly pressing. Kenya’s Data Protection Act (2019) and the Central Bank’s Digital Credit Regulations (2022) require providers to obtain explicit consent and undergo regular audits, but enforcement gaps persist.
Another pressing issue is over-indebtedness. Easy access to credit has trapped some borrowers in cycles of repeat borrowing. Digital overdraft products like Fuliza illustrate the risk: while they provide quick cash, default rates in certain segments have ranged between 10% and 21%.
If fintechs and banks can address issues of bias, transparency, and debt sustainability, AI-driven credit systems may become the backbone of Kenya’s financial future, unlocking billions in untapped credit while deepening financial inclusion for underserved communities.
Jefferson Wachira is a writer at Africa Digest News, specializing in banking and finance trends, and their impact on African economies.