FSD UgandaWhen risk management creates exclusion: Rethinking digital credit for vulnerable borrowers
By Andrew Kasujja
Digital credit has transformed financial inclusion across Uganda. For millions of people, access to formal credit no longer requires visiting a bank branch, completing lengthy paperwork or providing traditional collateral. A mobile phone has become the gateway to financial services that were once inaccessible to much of the population.
Yet an important paradox is beginning to emerge.
Can a system designed to promote financial inclusion unintentionally create a new form of financial exclusion?
Digital lenders have legitimate reasons to manage credit risk. Automated lending decisions rely on repayment behaviour to determine future borrowing limits, pricing and eligibility. Borrowers who repay on time are rewarded with larger loan limits and improved access to credit. Those who default may face reduced borrowing limits, higher charges or negative credit reporting. These mechanisms are fundamental to maintaining portfolio quality and protecting lenders from excessive losses.
When temporary shocks become financial exclusion
The difficulty arises when digital systems cannot distinguish between borrowers who will not repay and borrowers who simply could not earn income temporarily.
Consider a trader whose sales collapse after several days of heavy rain, or a boda boda rider whose income falls because of temporary transport restrictions. Their repayment behaviour reflects a short-term liquidity shock rather than permanent financial distress. Yet algorithmic credit models often treat both situations identically.
The consequences can be severe.
Reduced borrowing limits, negative credit records and restricted future access to credit may leave otherwise responsible borrowers unable to finance inventory, purchase agricultural inputs or continue operating their businesses. What begins as a temporary income interruption can evolve into prolonged financial exclusion.
Ironically, the very tools designed to manage lending risk may increase economic vulnerability.
What other markets are doing differently
This challenge is not unique to Uganda. In Kenya, several digital lenders have progressively moved beyond purely repayment-based credit scoring by incorporating broader mobile money transaction histories, merchant payments and customer behaviour into lending decisions. These richer datasets provide a more comprehensive understanding of borrowers’ financial capacity than missed repayments alone.
In India, digital lending innovation increasingly leverages the country’s digital public infrastructure, including identity, payments and consent-based data-sharing, to assess borrowers using multiple data sources rather than relying exclusively on historical defaults. The emerging account aggregator framework enables borrowers to securely share financial information from different institutions, allowing lenders to make more informed lending decisions and reducing unnecessary exclusion for customers with limited traditional credit histories.
These examples demonstrate an important principle: better information produces better risk management.
What could change in Uganda?
Uganda can adopt similar approaches while tailoring them to local realities.
First, lenders should increasingly incorporate real-time cash-flow information rather than relying predominantly on repayment history. Mobile money transactions, merchant payment activity, utility payments and digital savings patterns can provide valuable insight into whether repayment difficulties reflect temporary disruption or long-term financial deterioration.
Second, digital credit products should better match borrower income cycles. Farmers should not be expected to repay according to the same schedule as salaried workers. Traders with seasonal income require different repayment structures from daily transport operators.
Third, digital savings and emergency liquidity products should accompany digital lending. Borrowers with small precautionary savings are significantly better positioned to absorb temporary income disruptions without falling into default.
Fourth, lenders should adopt graduated recovery mechanisms before imposing severe penalties. Short repayment extensions, restructuring options or temporary repayment holidays following verifiable external shocks can preserve both customer relationships and portfolio quality.
Fifth, regulators may wish to review whether existing digital credit reporting frameworks adequately differentiate temporary liquidity events from persistent default. Credit information systems should support responsible lending while avoiding unnecessary exclusion of otherwise creditworthy borrowers.
From access to resilient inclusion
The broader lesson is that digital finance should not simply automate traditional lending processes. It should improve them.
Technology enables lenders to collect more data, analyse borrower behaviour more intelligently and develop products that reflect how people earn incomes. Used effectively, artificial intelligence and alternative data can strengthen both financial inclusion and risk management simultaneously.
Uganda has made remarkable progress in expanding digital access to finance. The next frontier is ensuring that digital credit systems are sufficiently sophisticated to distinguish temporary hardship from genuine credit risk.
Financial inclusion should not end with the first missed repayment. The real measure of success is whether borrowers who experience temporary setbacks can recover, rebuild and continue participating in the formal financial system. That is the transition Uganda’s digital credit ecosystem must now make, from expanding access to building resilient inclusion.
