Credit Scoring with Social Network Data

Yanhao Wei (), Pinar Yildirim, Christophe Van den Bulte () and Chrysanthos Dellarocas ()
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Yanhao Wei: Department of Economics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Christophe Van den Bulte: Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Chrysanthos Dellarocas: Information Systems Department, Questrom School of Business, Boston University, Boston, Massachusetts 02215

Marketing Science, 2016, vol. 35, issue 2, 234-258

Abstract: Motivated by the growing practice of using social network data in credit scoring, we analyze the impact of using network-based measures on customer score accuracy and on tie formation among customers. We develop a series of models to compare the accuracy of customer scores obtained with and without network data. We also investigate how the accuracy of social network-based scores changes when consumers can strategically construct their social networks to attain higher scores. We find that those who are motivated to improve their scores may form fewer ties and focus more on similar partners. The impact of such endogenous tie formation on the accuracy of consumer scores is ambiguous. Scores can become more accurate as a result of modifications in social networks, but this accuracy improvement may come with greater network fragmentation. The threat of social exclusion in such endogenously formed networks provides incentives to low-type members to exert effort that improves everyone’s creditworthiness. We discuss implications for managers and public policy.

Keywords: social networks; credit score; customer scoring; social status; social discrimination; endogenous tie formation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:35:y:2016:i:2:p:234-258

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