A case for risk-based lending
What you need to know:
In 2021, Uganda’s lending rate averaged 9.68 percent, compared to Kenya’s 6.14 percent where risk-based lending is widely implemented
Lending interest rates and margins are influenced by various factors, including the cost of funds, macroeconomic conditions, operational costs, and risk. While these factors are important, risk and credit are fundamentally connected and should form the foundation of an effective credit market. In a well-functioning system, the cost of credit would directly reflect the level of risk involved in lending. Unfortunately, in many developing countries, including Uganda, this principle is not effectively applied, resulting in a disconnect between the actual risk of a borrower and the cost of credit. For example, Uganda had the highest interest rate margins in East Africa and the second highest in Africa, standing at 9.68 percent in 2021, surpassed only by Zimbabwe at 12.83 percent. In 2023, Uganda's interest rate margins ranged from 8 percent to 34 percent, averaging 10.76 percent. Limited credit data leads lenders to charge higher rates as a precaution—excluding or overcharging creditworthy borrowers.
In 2021, Uganda’s lending rate averaged 9.68 percent, compared to Kenya’s 6.14 percent where risk-based lending is widely implemented. Due to limited data or underutilisation of available data in making informed credit decisions, financial institutions in developing countries like Uganda often error on the side of caution, charging interest rates much higher than the actual risk profile of customers. While this cautious approach reduces lender risk, it frequently excludes many creditworthy individuals or unfairly penalises them. With reliable data on individual customer creditworthiness (capacity and willingness to pay), the cost drivers could be significantly reduced. This would enable creditworthy borrowers to access financing at more favourable terms such as lower rates, reduced collateral requirements or higher loan amounts. This approach, known as risk-based financing (RBF), emphasises that credit decisions should be based on the actual risk profile of the customer.
The case for adopting risk-based lending is clear- RBF not only promotes fairer lending but also enhances financial inclusion by making credit more accessible to deserving individuals. One major challenge facing Uganda’s credit market is information asymmetry—a gap where lenders lack full information about borrowers. This leads to mispriced risk, inflated interest rates, and ultimately, the exclusion of many creditworthy individuals from the formal lending system. Financial institutions often lack reliable data, making it difficult to assess creditworthiness. Without a robust credit reporting system, lenders rely on generalised assumptions, inflating interest rates for many borrowers. Several factors contribute to high information asymmetry in Uganda. These include: Underdeveloped credit information markets: Until 2024, the use of credit reference bureaus (CRBs) in Uganda was mandatory only for tier 1-3 institutions, leaving out most borrowers in the tier 4 space.
Additionally, non-financial credit providers, such as utility companies, landlords, supplier credit firms, rarely report credit information, further exacerbating the information gap. Identification challenges: Uganda lacked a standardised form of identification for financial services. Replacing financial cards, National ID numbers are now the standard for financial services, though adoption among Tier IV institutions remains inconsistent. Data formats: Historical credit data in hard copy limits digital analysis, reducing efficiency and scalability of lending decisions. Addressing information asymmetry To tackle the issue of information asymmetry, several efforts are underway. One significant step is the adoption of the National Identification Number (NIN) as the standard form of identification. As more credit providers such as utility companies and landlords adopt the NIN, additional data points will become available for assessing creditworthiness.
Raising awareness and onboarding Tier IV financial institutions onto credit information systems will help bring underserved borrowers into the credit ecosystem. The neutral role of Credit Reference Bureaus (CRBs) and the development of shared credit information infrastructure for a centralised, real-time credit data source. This kind of collaboration across the industry enhances transparency and enables institutions to better serve their customers. Leveraging alternative data sources, lenders are increasingly relying on alternative data sources, such as mobile network operators, social media activity, betting and using data analytics, to better predict creditworthiness. These data points are helping create predictive scores that guide lending decisions.
Edward Ssenkindu,
