Credit Scoring with Social Network Data

Motivated by the growing practice of using social network data in credit scoring, this study analyzes 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 individuals can strategically construct their social networks to attain higher credit scores. We find that, if individuals are motivated to improve their scores, they may form fewer ties and focus them on more similar partners. The impact of such endogenous tie formation on the accuracy of consumer credit scores is ambiguous. Scores can become more accurate as a result of modi fications 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 e ffort that improves everyone's creditworthiness. We discuss implications for both managers and public policy