Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data

Abstract

By leveraging machine-learning algorithms and using nontraditional digital data derived primarily from borrowers’ mobile devices, digital lenders have vastly expanded access to credit in developing economies for millions of individuals without a prior credit history. At the same time, short-term, high-interest-rate digital loans are often issued to borrowers with low financial literacy and are subject to weak consumer protection, raising concerns over predatory lending practices. To examine how access to digital credit influences borrowers’ financial well-being, we take advantage of a proprietary data provided by a large digital lender in Kenya that randomly approves loan applications that would have otherwise been rejected based on the borrower’s credit profile. We find that access to digital credit enhances the financial well-being of borrowers across a variety of mobile-phone-based well-being measures, including their monetary transaction volume, mobility, and social networks, as well as borrowers’ self-reported income and employment. Additionally, we provide some supporting evidence that access to digital credit leads to a greater improvement in financial well-being when borrowers have limited access to credit, take loans for business purposes, and obtain more credit. Overall, we demonstrate that access to short-term, high-interest-rate digital loans can benefit borrowers in developing economies.

Keywords: Digital Loan, Digital Credit, Access to Credit, Alternative Data, Financial Welfare, Financial Well-Being, Developing Economies, Mobile Phone Data

JEL Classification: D14, G21, G51, M40, M41, O16, O30, O55