Over 350 million adults across Africa remain without access to formal banking services, yet 60% of this population now owns a mobile phone as of 2024.
This gap between banking access and mobile usage has created an opportunity: AI-powered credit scoring systems that turn mobile money transaction data into financial opportunity.
Once, an average Kenyan citizen couldn’t get a loan because they had no bank account or credit history. Today, M-Pesa activity has helped them qualify for a business loan that increased their inventory.
The Power of Non-Traditional Data
When Joseph Kamau pays his electricity bill through mobile money, tops up his airtime weekly, and sends money to his rural parents monthly, he’s not just managing his finances, he’s building a credit profile.
AI credit scoring systems analyze these digital footprints to assess creditworthiness where bank statements don’t exist.
Tala, operating in Kenya and other African markets use alternative data to assess creditworthiness, leveraging mobile phone usage patterns and financial behavior to provide quick loan approvals
Traditional credit scoring looks backward at banking history. PayHippo’s system analyzes to assess creditworthiness through analyzing transaction frequency, amounts, spending categories, and timing patterns to create accurate risk profiles.
Branch International, another leading mobile lender, has disbursed over $600 million to more than 3 million customers across Kenya, Nigeria, and Tanzania.
Their algorithm considers factors as varied as mobile money transaction consistency, contact list diversity, and even battery charging habits to assess reliability and financial stability.
Democratizing Access to Credit
In Rwanda’s Eastern Province, Marie Uwase runs a successful poultry business funded through a series of microloans obtained via her mobile phone.
As a rural woman with no formal banking history, traditional financial institutions considered her “high risk.” Yet her consistent repayment record through mobile lending platforms tells a different story.
Marie’s experience highlights a critical advantage of AI credit scoring through mobile money: it reaches demographics traditionally excluded from financial services.
Women entrepreneurs, rural populations, and informal sector workers, who together form the backbone of many African economies, are gaining access to capital.
MFS Africa, a digital payments hub reaching across 35 African countries, has a significant number of women who are first-time borrowers.
In Tanzania, where agricultural workers comprise much of the unbanked population, mobile lender Tigo Nivushe has extended over 130,000 loans to farmers based on their mobile money activities rather than land ownership or formal employment.
“The monthly repayment rate on my fertilizer loan matches my harvest income pattern,” explains Amani Kipenza, a smallholder farmer. “Traditional banks wanted payments that didn’t match my cash flow reality.”
By assessing ability to repay through actual financial behavior rather than formal documentation, AI-powered mobile lending aligns with the economic realities of Africa’s unbanked population.
The Risk Assessment in the African Context
Standard credit scoring models developed in Western markets often fail in African contexts where financial behaviors differ significantly. AI systems developed specifically for mobile money lending in Africa consider unique regional factors.
“In Northern Uganda, we noticed regular small transfers to multiple recipients indicated family support networks—a positive factor for creditworthiness in our algorithm,” notes Daniel Ochan of Ensibuuko, a Ugandan microfinance technology provider. “Western models might flag this as suspicious activity.”
Jumo, operating across several African markets, has developed AI models specific to different regions and economic activities. Their system recognizes that a taxi driver in Accra shows financial consistency differently than a market seller in Nairobi or a teacher in Lusaka.
These contextual adaptations produce significant improvements.
When South African fintech Lulalend incorporated regional business payment cycles into their credit algorithm, approval rates for informal retailers increased by 30% while default rates decreased.
The most advanced platforms continually refine their models based on regional performance. M-Shwari in Kenya adjusts its scoring algorithm during agricultural seasons to account for different cash flow patterns among farming communities. This contextual understanding leads to more appropriate loan terms and higher repayment rates.
Trust, Transparency, and Ethical Considerations
Despite its benefits, AI credit scoring through mobile money raises important questions about transparency and ethics.
Leading providers are addressing these concerns through clearer communication. Safaricom’s M-Shwari now provides users with specific reasons when loan applications are rejected, helping customers understand which behaviors affect their creditworthiness.
Consumer protection remains a priority. Kenya’s recent interest rate caps on digital loans followed concerns about predatory lending practices. Industry leaders like Branch and Tala responded by introducing clearer terms, graduated loan limits, and financial education components to their platforms.
The Road Ahead
As AI credit scoring through mobile money continues expanding across Africa, collaboration between fintechs, telecom companies, regulators, and consumer advocates will shape its development.
In countries where regulatory frameworks support responsible mobile lending, financial inclusion rates have increased by up to 20% in just five years. These gains represent not just statistics but transformed lives and livelihoods.
For the continent’s unbanked millions, the combination of mobile money and AI doesn’t just offer loans, it provides dignity, opportunity, and entry into the formal economy on their terms.
This represents financial inclusion in its truest form: meeting people where they are with tools designed for their actual needs and circumstances.
