Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

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...

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Main Authors:	Bjorkegren, Daniel, Grissen, Darrell
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2019
Subjects:	CREDIT SCORING MACHINE LEARNING DIGITAL CREDIT MOBILE PHONE FINANCIAL INCLUSION
Online Access:	http://documents.worldbank.org/curated/en/811881575657172759/Behavior-Revealed-in-Mobile-Phone-Usage-Predicts-Credit-Repayment http://hdl.handle.net/10986/33018

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recordtype	oai_dc
spelling	okr-10986-330182022-09-20T00:13:28Z Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment Bjorkegren, Daniel Grissen, Darrell CREDIT SCORING MACHINE LEARNING DIGITAL CREDIT MOBILE PHONE FINANCIAL INCLUSION 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. 2019-12-13T21:25:08Z 2019-12-13T21:25:08Z 2019-12 Working Paper http://documents.worldbank.org/curated/en/811881575657172759/Behavior-Revealed-in-Mobile-Phone-Usage-Predicts-Credit-Repayment http://hdl.handle.net/10986/33018 English Policy Research Working Paper;No. 9074 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	CREDIT SCORING MACHINE LEARNING DIGITAL CREDIT MOBILE PHONE FINANCIAL INCLUSION
spellingShingle	CREDIT SCORING MACHINE LEARNING DIGITAL CREDIT MOBILE PHONE FINANCIAL INCLUSION Bjorkegren, Daniel Grissen, Darrell Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
relation	Policy Research Working Paper;No. 9074
description	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.
format	Working Paper
author	Bjorkegren, Daniel Grissen, Darrell
author_facet	Bjorkegren, Daniel Grissen, Darrell
author_sort	Bjorkegren, Daniel
title	Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
title_short	Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
title_full	Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
title_fullStr	Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
title_full_unstemmed	Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment
title_sort	behavior revealed in mobile phone usage predicts credit repayment
publisher	World Bank, Washington, DC
publishDate	2019
url	http://documents.worldbank.org/curated/en/811881575657172759/Behavior-Revealed-in-Mobile-Phone-Usage-Predicts-Credit-Repayment http://hdl.handle.net/10986/33018
_version_	1764477817972588544

Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

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