# Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

arXiv: https://arxiv.org/abs/1712.05840 · HTML: https://mlsanity.com/paper/1712.05840/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting](https://mlsanity.com/paper/2608.16856/) (2026)
- [Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation](https://mlsanity.com/paper/2606.00827/) (2026)
- [zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring](https://mlsanity.com/paper/2609.08247/) (2026)

## Abstract (arXiv)

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.