Socio-Demographic Determinants of Default Rate among Digital Lending Platform Borrowers: An Emerging Market Perspective

Abstract

This paper explores the influence of socio-demographic characteristics namely gender, age, education level, and income on the default rates among borrowers using digital credit platforms in Nairobi County, Kenya. Using statistical discrimination theory and credit theory as a foundation, the study adopted a cross-sectional design, drawing data from a sample of mobile phone owners in the Kasarani Sub-County. Data was derived from 387 respondents and 210 secondary records. The study uses binary logistic regression to analyze the relationship between socio-demographic factors and loan default rates. The results indicate that female borrowers are 35.5% less likely to default than males. Borrowers aged 36 to 60 and over 61 years old are less likely to default than younger borrowers. Furthermore, higher education levels, particularly advanced degrees, and income levels above USD 560 are associated with reduced odds of default. The study concludes that gender, age, education, and income are significant predictors of loan default and suggests that digital lenders adjust their credit risk models accordingly.