Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

Abstract

Many households in developing countries lack formal financial histories, making it difficult for banks to allocate capital, and for potential borrowers to obtain loans. However, many unbanked households have mobile phones, and even prepaid phones generate rich data about their behavior. This project shows that behavioral signatures in mobile phone data predict default with accuracy approaching that of credit scoring methods that rely on financial histories. The method is demonstrated using call records matched to loan outcomes for a sample of borrowers in a Caribbean country.

Keywords: credit scoring, microfinance, mobile phones, big data

JEL Classification: O16