{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:23:58Z","timestamp":1779384238507,"version":"3.53.1"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71603259"],"award-info":[{"award-number":["71603259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009625","name":"Beijing Social Science Fund","doi-asserted-by":"publisher","award":["19GLA002"],"award-info":[{"award-number":["19GLA002"]}],"id":[{"id":"10.13039\/501100009625","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform\u2019s capacity to build effective credit scoring. In recent years, many types of data are used for credit scoring to compensate for the lack of credit history data. Whether social network information can be used to strengthen financial institutions\u2019 predictive power has received much attention in the industry and academia. The aim of this study is to test the reliability of social network information in predicting loan default. We extract borrowers\u2019 social network information from mobile phones and then use logistic regression to test the relationship between social network information and loan default. Three machine learning algorithms\u2014random forest, AdaBoost, and LightGBM\u2014were constructed to demonstrate the predictive performance of social network information. The logistic regression results show that there is a statistically significant correlation between social network information and loan default. The machine learning algorithm results show that social network information can improve loan default prediction performance significantly. The experiment results suggest that social network information is valuable for credit scoring.<\/jats:p>","DOI":"10.3390\/info10120397","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T03:19:36Z","timestamp":1576811976000},"page":"397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending"],"prefix":"10.3390","volume":"10","author":[{"given":"Beibei","family":"Niu","sequence":"first","affiliation":[{"name":"College of Economics and Management, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzheng","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1111\/1756-2171.12019","article-title":"The impact of credit scoring on consumer lending","volume":"44","author":"Einav","year":"2013","journal-title":"Rand J. 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