Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data
Chen, AJ Yuan, Even-Tov, Omri, Kang, Jung Koo, Wittenberg-Moerman, Regina
The Accounting Review · 2025 · Volume 100 · Issue 4
Abstract
To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of individuals lacking a prior credit history. However, short-term, high-interest digital loans have raised concerns about predatory lending practices. To examine how digital credit influences borrowers’ financial well-being, we use proprietary data from a 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 improves borrowers’ financial well-being across various mobile-phone-based well-being measures, including monetary transactions and balances, mobility, and social networks as well as borrowers’ self-reported income and employment. We further show that this positive impact is more pronounced when borrowers have limited access to credit, take loans for business purposes, and obtain more credit. JEL Classifications:D14; G21; G51; M40; M41; O16; O30; O55.
Keywords
information asymmetry, alternative Big Data, digital loan, digital credit, access to credit, financial welfare, financial well-being, developing economies, mobile phone data
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