AN EMPIRICAL ANALYSIS OF LOAN REPAYMENT BEHAVIOR AND DEFAULT RATES ON DIGITAL LENDING PLATFORMS: EVIDENCE FROM AN EMERGING MARKET

Abstract

The rapid growth of digital lending platforms has revolutionized access to credit in emerging economies, providing financial opportunities to underserved individuals. Despite these advancements, high loan default rates remain a substantial challenge, threatening the sustainability of these lending platforms and the broader financial ecosystem. This study examines the determinants of loan default among users of mobile-based digital lending platforms in Kenya, a leading adopter of digital financial services. Employing a sample of 161 responses from borrowers, the analysis uses a probit regression model to identify primary predictors of loan default. The study focuses on borrower demographics like age and place of residence and loan characteristics, such as repayment term (period) and interest rates. The results reveal that older customers and those in rural areas are more likely to default on their loans, whereas longer repayment periods reduce the probability of default. Interestingly, the interest rate does not significantly affect default behavior, implying that borrowers prioritize access to credit over its cost. These results underscore the essence of tailoring digital lending practices to the diverse borrower needs and circumstances. By addressing the factors that drive loan defaults, digital platform lenders can improve risk assessment frameworks, enhance financial inclusion, and enhance the sustainability of digital lending platforms. The findings provide actionable insights for policymakers, lenders, and other industry players seeking to mitigate default risks and foster a more robust digital financial ecosystem