Socio-Demographic Determinants of Default Rate Among Digital Lending Platform Borrowers in Nairobi County, Kenya

Abstract

Digital loans play a significant role in enabling access to credit for the digital borrowers. However, the growth in digital loans has resulted in increased number of Kenyans defaulting on their loan repayment. The main objective of the study was to assess the socio-demographic determinants of default rate of digital credit platforms borrowers in Nairobi County, Kenya. The specific objectives were to examine the effect of gender, age, education and income level on default rate of digital credit platforms borrowers in Nairobi County, Kenya. The study was guided by the statistical discrimination and credit theory. The study adopted a cross-sectional research design. The study was conducted in Kasarani sub-county, Nairobi County. The target population of the study was 281,342 owners of mobile phones in Kasarani sub-county. Multistage sampling method was used to sample the respondents. The study used both quantitative and qualitative data. Binary logistics regression model was used to establish the sociodemographic characteristics of borrowers that are associated with loan default rate. The findings indicated that female borrowers are approximately 35.5% less likely to default compared to male borrowers. Borrowers aged 36-60 years and those aged 61 years and above are less likely to default compared to the base reference category of 18-35 years. The findings further revealed that borrowers with advanced degrees (Master's and Ph.D.) exhibit a lower odd of default compared to those with no education. The findings indicated that income levels in the range of $.560 - 650 and $.660 and above have a significant influence on the likelihood of default. The study concluded that the socio-demographic factors (gender, age, education, and income level) are significant determinants of the default rate among digital lending platforms in Kenya. The study recommends that lending policy options should be directed towards enabling borrowers to upgrade their socio-economic characteristics. Further, digital credit platforms should calibrate their lending strategies to accommodate the credit needs of older borrowers, ensuring they receive fair and appropriate credit options tailored to their financial capabilities.