In a Nairobi market, a fruit vendor named Achieng’ taps her phone and secures an instant loan to stock her stall, no paperwork, no bank officer, just an app.
Within seconds, a credit scoring AI algorithm has combed through her mobile money transactions, airtime top-ups, and even how promptly she pays her phone bill. Loan approved. Money lands in her M-Pesa wallet.
This everyday scene captures Kenya’s digital lending boom in action. Across the country, fintech lenders are using artificial intelligence to redefine credit scoring, bringing millions of “credit-invisible” Kenyans into the financial fold. The journey is exciting, data-driven, and not without its ethical twists and regulatory turns. Let’s dive into how it all works and why it matters to you.
Traditional credit scores rely on bank loans, payslips, or credit card histories things many Kenyans simply don’t have. Today’s lending apps assess your creditworthiness by analyzing the digital bread crumbs of daily life: mobile money flows, phone usage, social media habits, e-commerce activity, and more. If it lives on your phone, it might help get you a loan.
How does AI help Credit Scoring?
Fintech innovators like Tala and Branch have built AI models that crunch hundreds of data points from a user’s smartphone. Tala’s Android app, for instance, looks at everything from the make of your phone to the frequency of your utility payments, using over 250 micro-indicators to generate a credit score.
It’s not spying users opt in, sharing data in exchange for the chance to borrow. Branch, similarly, examines handset details, SMS logs, GPS data, contact lists, and more, using machine learning to find patterns that correlate with repayment behavior.
“By using machine learning to analyze alternative data… Branch is able to reliably predict loan repayment in markets with limited bureau coverage and a huge segment of first-time borrowers,” explains Matthew Flannery, CEO of Branch.
The power of these new credit algorithms is that they learn and adapt. Over time, as you take loans and repay, the AI refines your credit score. If you deepen your relationship with the lender (say, by borrowing and repaying consistently), you unlock larger loans at better terms. It’s a dynamic, real-time credit scoring system, a far cry from the static, one-size-fits-all credit checks of the past
This speed and convenience have made digital lending wildly popular. Even Kenya’s largest telco Safaricom jumped in with services like M-Shwari and Fuliza.
As Shivani Siroya, Tala’s founder, put it in a TED talk: “With something as simple as a credit score, we’re giving people the power to build their own futures.”
Big banks are also blending alternative data into their risk models. In 2023, TransUnion Kenya and analytics firm FICO launched a new scoring system using 145 data points and 24 months of financial behavior to rate borrowers. IBM Research Africa has worked on AI models that analyze 10 million mobile phone records to derive credit insights.
Ethical and Regulatory Hurdles for Credit Scoring
ike any revolution, the AI lending boom has its growing pains. As Kenya leaps ahead in fintech, regulators and civil society are asking tough questions: Are these algorithms fair? Is my personal data safe? What happens if the AI gets it wrong?
- •Data Privacy: Early on, some lenders abused data access. Kenya responded with the Data Protection Act (2019) and CBK’s Digital Credit Providers (DCP) regulations (2022) now demand customer privacy. Trust hinges on robust data safeguards.
- •Algorithmic Bias: Algorithms can reflect societal biases. Experts are urging caution and calling for algorithm audits to prevent digital discrimination.
- •Black Box Transparency: Many AI systems operate as black boxes. Innovation in “explainable AI” could offer a balance between clarity and security.
- •Regulatory Response: Kenya has reined in rogue digital lenders through licensing. Now, there’s a push for CBK to issue guidelines on ethical AI use in credit scoring.
Algorithm for Inclusion
Kenya’s experience shows that AI can be a powerful engine for financial inclusion if we tune it right. Tens of thousands of Kenyans each day are getting loans they’d never have obtained from a bank, thanks to algorithms that see potential where traditional metrics saw none.
Yet, the question remains: who watches the algorithms? For Kenya’s credit revolution to remain smart, it will need more than cutting-edge technology. It will require human wisdom and ethical foresight.
