Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace

Hongming Gao, Hongwei Liu, Carol Ou, P.A. Pavlou, H. Zhu, Mingjun Zhan

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Online micro-loan marketplaces cannot accurately and effectively approve loan applications due to the uncertain opportunistic behavior of loan applicants and the possibility of loan defaults by loan applicants. To address this challenge, in this study we integrate signaling theory, the social structure of competition, and the concept of homophily to develop a research model to predict a loan applicants re-loaning success by examining his/her financial status, friendship network characteristics, and friendship network centrality. Data of 683 anonymous and distinct loan applicants at a major online micro-loan marketplace in China largely support our hypotheses, highlighting the three key signals of a successful re-loan approval, a loan applicants credit card default, the number and the percentage of his/her friends with re-loan approvals in the focal micro-loan marketplace. Research and practical implications are discussed.
Original languageEnglish
Title of host publicationProceedings of 24th Americas Conference on Information Systems (2018 AMCIS)
Subtitle of host publicationDigital Disruption
Place of PublicationNew Orleans
PublisherAssociation for Information Systems
Publication statusPublished - Aug 2018

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Loans
Friendship
Network centrality
Social structure
Opportunistic behavior
China
Credit cards
Homophily
Signaling theory

Cite this

Gao, H., Liu, H., Ou, C., Pavlou, P. A., Zhu, H., & Zhan, M. (2018). Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. In Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption [14] New Orleans: Association for Information Systems.
Gao, Hongming ; Liu, Hongwei ; Ou, Carol ; Pavlou, P.A. ; Zhu, H. ; Zhan, Mingjun. / Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption. New Orleans : Association for Information Systems, 2018.
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abstract = "Online micro-loan marketplaces cannot accurately and effectively approve loan applications due to the uncertain opportunistic behavior of loan applicants and the possibility of loan defaults by loan applicants. To address this challenge, in this study we integrate signaling theory, the social structure of competition, and the concept of homophily to develop a research model to predict a loan applicants re-loaning success by examining his/her financial status, friendship network characteristics, and friendship network centrality. Data of 683 anonymous and distinct loan applicants at a major online micro-loan marketplace in China largely support our hypotheses, highlighting the three key signals of a successful re-loan approval, a loan applicants credit card default, the number and the percentage of his/her friends with re-loan approvals in the focal micro-loan marketplace. Research and practical implications are discussed.",
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Gao, H, Liu, H, Ou, C, Pavlou, PA, Zhu, H & Zhan, M 2018, Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. in Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption., 14, Association for Information Systems, New Orleans.

Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. / Gao, Hongming; Liu, Hongwei; Ou, Carol; Pavlou, P.A.; Zhu, H.; Zhan, Mingjun.

Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption. New Orleans : Association for Information Systems, 2018. 14.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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AU - Zhan, Mingjun

PY - 2018/8

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N2 - Online micro-loan marketplaces cannot accurately and effectively approve loan applications due to the uncertain opportunistic behavior of loan applicants and the possibility of loan defaults by loan applicants. To address this challenge, in this study we integrate signaling theory, the social structure of competition, and the concept of homophily to develop a research model to predict a loan applicants re-loaning success by examining his/her financial status, friendship network characteristics, and friendship network centrality. Data of 683 anonymous and distinct loan applicants at a major online micro-loan marketplace in China largely support our hypotheses, highlighting the three key signals of a successful re-loan approval, a loan applicants credit card default, the number and the percentage of his/her friends with re-loan approvals in the focal micro-loan marketplace. Research and practical implications are discussed.

AB - Online micro-loan marketplaces cannot accurately and effectively approve loan applications due to the uncertain opportunistic behavior of loan applicants and the possibility of loan defaults by loan applicants. To address this challenge, in this study we integrate signaling theory, the social structure of competition, and the concept of homophily to develop a research model to predict a loan applicants re-loaning success by examining his/her financial status, friendship network characteristics, and friendship network centrality. Data of 683 anonymous and distinct loan applicants at a major online micro-loan marketplace in China largely support our hypotheses, highlighting the three key signals of a successful re-loan approval, a loan applicants credit card default, the number and the percentage of his/her friends with re-loan approvals in the focal micro-loan marketplace. Research and practical implications are discussed.

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Gao H, Liu H, Ou C, Pavlou PA, Zhu H, Zhan M. Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. In Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption. New Orleans: Association for Information Systems. 2018. 14