Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

TL;DR

This study demonstrates that behavioral patterns in mobile phone usage can effectively predict loan default, outperforming traditional credit bureau models, especially for unbanked populations in developing countries.

Contribution

It introduces a novel method using mobile phone data to predict credit default, applicable even to individuals without financial histories.

Findings

Mobile phone behavior predicts default with high accuracy.

Method outperforms traditional credit bureau models.

Unbanked individuals can be effectively scored for credit risk.

Abstract

Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone data predict default, using call records matched to repayment outcomes for credit extended by a South American telecom. On a sample of individuals with (thin) financial histories, our method actually outperforms models using credit bureau information, both within time and when tested on a different time period. But our method also attains similar performance on those without financial histories, who cannot be scored using traditional methods. Individuals in the highest quintile of risk by our measure are 2.8 times more likely to default than those in the lowest quintile. The method forms the basis for new forms of credit that reach the unbanked.