How gnuGrid CRB’s Mobile Credit Score Is Redrawing Uganda’s Financial Inclusion Map and Helping Lenders De-Risk the Last Mile

In Uganda’s bustling and often chaotic last-mile credit markets, where boda-boda riders, gig workers, and micro-entrepreneurs hustle daily without collateral or formal financial records, a quiet revolution is reshaping how lenders assess risk—and how underserved individuals access capital. At the heart of this transformation is a small but mighty innovation: gnuGrid CRB’s Mobile Credit Score.

This mobile-powered credit score is not just plugging data gaps—it’s redrawing Uganda’s financial inclusion map, enabling digital lenders like Mogo Uganda to serve high-risk populations with more confidence and efficiency. The result? Better-performing loan books, reduced default rates, and a new path to dignity for thousands who were once invisible to traditional finance.

A Data-Driven Revolution for the Informal Sector

“Our business operates with customers who are not served by the traditional banking system,” explains Mikhail Vydryn, CEO of Mogo Uganda, a subsidiary of Eleving Group. “So how do you understand which customer is potentially good and which one is potentially bad?”

That question lay at the heart of Mogo Uganda’s challenge. Since 2019, the asset-based lender has focused on providing boda-boda (motorcycle), car logbook, and smartphone loans—particularly to self-employed Ugandans with no formal credit history. But the missing piece was data—reliable, real-time indicators of creditworthiness for a population largely excluded from traditional financial records.

Enter gnuGrid CRB’s Mobile Credit Score, an AI-powered scoring tool that taps into behavioral data from mobile network operators (MNOs), such as airtime usage, top-ups, and mobile phone activity patterns.

“It actually makes the business much easier and better for us,” says Vydryn. “gnuGrid CRB’s Mobile Credit Score is part of our scoring engine. The correlation is pretty clear—if a customer has a higher rating on the gnuGrid score, all his indicators, be it PDS or any other indicator, will be much better”.

Reducing Risk and Improving Bottom Lines

Since adopting gnuGrid CRB’s score more than 18 months ago, Mogo Uganda’s loan book has undergone a noticeable transformation.

“We’ve seen that our NPLs [non-performing loans] are much better,” Vydryn continues. “Especially for first-payment defaults and early-stage delinquencies, the score helps us predict behavior pretty well”.

The gnuGrid CRB system assigns each borrower a credit grade (AAA, ABB, etc.), enabling lenders to make tiered decisions: higher-rated customers receive better offers—lower down payments and interest rates,while higher-risk applicants are either given stricter terms or politely turned away.

“For boda-boda financing, customers are divided into five groups,” Vydryn explains. “The best one gets the lowest down payment, currently UGX 700,000. The riskiest group pays up to UGX 2 million upfront. That’s how we balance the risk profile. We’ve financed more than 80,000 boda-boda customers in Uganda since day one, so we were able to learn and update the model accordingly”.

The result is a smarter, leaner lending operation. Mogo Uganda now approves more borrowers faster, while reducing defaults—especially in their flagship motorcycle loan product. Their smartphone loan product alone has already issued over 3,000 loans, a figure expected to rapidly grow thanks to data-driven onboarding.

The Score That Empowers the Invisible

For David Opio, ED and co-founder of gnuGrid CRB, these outcomes are validation of a broader vision: using alternative data to bring Uganda’s financially invisible into the fold.

“The data has become the reputation collateral, the true representation of a customer’s behaviour,” Opio asserts. “Our score predicts default risk. If we say Client X is a triple-A, he has a default rate of 10%. That gets put into practice—and lenders like Mogo Uganda experience that result”.

Opio says institutions that use gnuGrid CRB’s scoring tools consistently post NPL rates below 5%—well below industry averages. “I believe institutions using our scoring have the lowest non-performing loans,” he notes.

gnuGrid CRB’s score draws from both conventional data—aggregated from 33 regulated financial institutions under the Bank of Uganda—and alternative data from MNOs like Airtel. The latter, according to Opio, “speaks about behaviour but also the ability of a customer to pay,” offering critical insights where formal credit records are absent.

And this impact goes far beyond numbers. The score gives consumers a pathway to access credit—and a reason to improve their financial behavior.

“Based on someone’s rating, Mogo Uganda is able to determine the interest rate and down payment,” Opio continues. “Those are incentives that encourage customers to improve their behaviour and work towards a better rating”.

Unlocking Cross-Border Impact

Having proven its effectiveness in Uganda, the gnuGrid CRB model is gaining traction beyond borders.

“Mogo Uganda has demonstrated interest in scaling what we are doing for them here into other markets, like Kenya and Tanzania,” says Opio. “They want to walk with us in those markets because they’ve seen the impact of what we’ve done in Uganda”.

Vydryn confirms the interest: “We see strong potential for cross-border application… especially in markets with similar credit visibility challenges,” he notes. “If we can get local MNO partnerships and regulatory alignment, gnuGrid CRB’s solution could be key to our East African growth strategy”.

A Catalyst for Market Discipline

Uganda’s financial services environment is also evolving alongside innovations like gnuGrid CRB.

“Uganda is one of the best countries in our group in terms of payment behaviour,” Vydryn reflects. “There are rules of the game now—it’s no longer the Wild West. The customer attitude toward loans has improved. We’ve moved from UMRA to being regulated by the Ministry of Finance, and that has brought structure to the market”.

Mogo Uganda’s financial literacy campaigns, launched in Uganda and Kenya, further support this transformation. “We openly tell customers—if you can’t afford a loan, don’t take it. And if you do, take responsibility to repay,” he emphasizes. “Before, people would default and just switch SIM cards. But now, more data means more responsibility—and better loan terms for good borrowers”.

Scaling Smarter, Serving Better

The gnuGrid CRB-Mogo Uganda collaboration is an early but compelling case study in how technology can tackle systemic exclusion. By blending behavioral data, AI analytics, and inclusive lending models, they are not only mitigating lender risk—they’re expanding opportunity.

“Risk control has been a key driver of our group’s success,” Vydryn says, referencing Eleving Group’s recent $14 million dividend payout. “In Uganda, tools like gnuGrid CRB’s MNO Score have allowed us to scale responsibly—serving more customers without increasing risk”.

Back in Kampala, Opio sees even greater possibilities: “We’ve proven that data can be an asset in bringing the invisible into the bigger financial ecosystem,” he says. “The gnuGrid Mobile Credit Score is not just about credit—it’s about dignity, visibility, and economic participation.”

As Uganda and its peers continue their march toward digital financial inclusion, tools like the gnuGrid Mobile Credit Score are fast becoming the compass. And for the millions at the margins, that could be the difference between hustling and thriving.

About the gnuGrid Mobile Credit Score

The gnuGrid Mobile Credit Score is Uganda’s first-ever mobile-powered credit scoring system, launched by gnuGrid CRB—the country’s first and only indigenous credit reference bureau licensed by the Bank of Uganda—in partnership with Airtel Mobile Commerce Uganda Limited (AMCUL). Developed with regulatory guidance from the Bank of Uganda and the Uganda Microfinance Regulatory Authority (UMRA), the solution marks a transformative shift in credit risk assessment and financial inclusion, particularly for last-mile and underserved populations.

Unlike traditional credit reports that rely solely on historical data from formal financial institutions, the gnuGrid Mobile Credit Score draws from over 400 alternative data points, including mobile money activity, airtime usage, and digital behaviour. This hybrid model integrates conventional data from 33 regulated financial institutions with real-time alternative data from telecom partners like Airtel (AMCUL), creating a holistic and inclusive assessment of creditworthiness.

The score is designed to empower both lenders and borrowers:

Lenders benefit from improved borrower segmentation, lower non-performing loans (NPLs), reduced operational costs, and increased efficiency in loan origination, disbursement, and repayment monitoring. The system enables more precise risk filtering and facilitates the development of tailor-made financial products. Over time, these efficiencies are expected to reduce the cost of money for genuine and creditworthy borrowers.

Borrowers, particularly those previously excluded from formal credit systems, gain instant visibility into their financial health. They can use their mobile devices to access, track, and grow their scores—unlocking access to larger and more affordable loans, and building formal financial identities based on digital behaviour rather than traditional collateral.

The Mobile Credit Score is closely aligned with Uganda’s National Financial Inclusion Strategy (NFIS) II (2023–2028), which targets an increase in access to formal financial services from 66% (2021) to 75% (2028) and private credit bureau coverage from 6.9% (2019) to 15% (2028). It also addresses the stark disparity identified in the World Bank’s Global Findex Database (2021), where although 77% of Ugandans borrow money, only 31% do so through formal institutions.

By leveraging mobile technology, behavioural data, and artificial intelligence, the gnuGrid Mobile Credit Score is redefining creditworthiness, breaking long-standing access barriers, and enabling a more inclusive, data-driven lending ecosystem across Uganda’s financial landscape.