[1] 中国人民银行征信中心.征信系统建设运行报告(2004-2014)[EB/OL]. (2015-03).http://www.pbccrc.org.cn/zxzx/zxzs/201508/f4e2403544c942cf99d3c71d3b 559236/files/0e78bdbd53cf4ed39b25d886a16054c9.pdf [2] Stiglitz J E, Weiss A. Credit rationing in markets with imperfect information[J]. The American Economic Review, 1981, 71(3): 393-410. [3] Avery R B, Bostic R W, Samolyk K A. The role of personal wealth in small business finance[J]. Journal of Banking & Finance, 1998, 22: 1019-1061. [4] 李焰, 高弋君, 李珍妮,等. 借款人描述性信息对投资人决策的影响:基于P2P网络借贷平台的分析[J]. 经济研究, 2014(1): 143-155.Li Yan, Gao Yijun, Li Zhenni, et al. The influence of borrower’s description on investors’ decision- Analyze based on P2P online lending[J]. Economic Research Journal, 2014(1): 143-155. [5] 李焰, 张迎新, 王琳.声誉的信息含量——来自P2P网络借贷的证据[J]. 管理评论, 2019, 31: 3-18.Li Yan, Zhang Yingxin, Wang Lin. The information content of reputation- Evidence from peer-peer lending[J]. Management Review, 2019, 31: 3-18. [6] 王会娟, 廖理.中国 P2P 网络借贷平台信用认证机制研究——来自“人人贷”的经验证据[J]. 中国工业经济, 2014, 4: 136-147.Wang Huijuan, Liao Li. Chinese P2P platform’s credit authentication mechanism research-Evidence from Renrendai[J]. China Industrial Economics, 2014, 4: 136-147. [7] Michels J. Do unverifiable disclosures matter? Evidence from peer-to-peer lending[J]. The Accounting Review, 2012, 87(4): 1385-1413. [8] Iyer R, Khwaja A L, Luttmer E F P, et al. Screening peers softly: Inferring the quality of small borrowers[J]. Management Science, 2016, 62(6): 1554-1577. [9] Seneviratne S, Seneviratne A, Mohapatra P, et al. Predicting user traits from a snapshot of apps installed on a smartphone[J]. ACM SIGMOBILE Mobile Computing and Communications Review, 2014, 18(2): 1-8. [10] Xu Runhua, Frey R M, Fleisch E, et al. Understanding the impact of personality traits on mobile app adoption - Insights from a large-scale field study[J]. Computers in Human Behavior, 2016, 62: 244-256. [11] Frey R M, Xu Runhua, Ilic A. Mobile app adoption in different life stages: An empirical analysis[J]. Pervasive and Mobile Computing, 2017, 40: 512-527. [12] Duarte J, Siegel S, Young L. Trust and credit: The role of appearance in peer-to-peer lending[J]. Review of Financial Studies, 2012, 25(8): 2455-2484. [13] 陈林, 谢彦妩,李平, 等. 借款陈述文字中的违约信号—基于P2P网络借贷的实证研究[J]. 中国管理科学, 2019, 27(4): 37-47.Chen Lin, Xie Yanwu, Li Ping, et al. The signal of default risk from the description-text based on the empirical research of P2P Lending[J]. Chinese Journal of Management Science, 2019, 27(4): 37-47. [14] 王小燕, 张中艳, 马双鸽. 基于文本先验信息的贷款信用风险评估模型[J]. 中国管理科学, 2021, 29(5): 34-44.Wang Xiaoyan, Zhang Zhongyan, Ma Shuangge. A loan credit risk model incorporating text prior information [J]. Chinese Journal of Management Science, 2021, 29(5): 34-44. [15] Lin Mingfeng, Prabhala N R, Viswanathan S. Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending[J]. Management Science, 2013, 59(1): 17-35. [16] Ge Ruyi, Feng Juan, Gu Bin, et al. Predicting and deterring default with social media information in peer-to-peer lending[J]. Journal of Management Information Systems, 2017, 34(2): 401-424. [17] Xu Runhua, Frey R M, Vuckovac D, et al. 2015. Towards understanding the impact of personality traits on mobile app adoption-a scalable approach[C]//Proceedings of 23rd European Conference on Information Systems, Münster, Germany, May 26-29, 2015. [18] Lee Y, Park I, Cho S, et al. Smartphone user segmentation based on app usage sequence with neural networks[J]. Telematics and Informatics, 2018, 35: 329-39. [19] Frey R, Xu Runhua, Ammendola C,et al. Mobile recommendations based on interest prediction from consumer's installed apps-insights from a large-scale field study[J]. 2017, Information Systems, 71: 152-163. [20] Berg T, Burg V, Gombovic' A, et al. On the rise of FinTechs: Credit scoring using digital footprints[J]. The Review of Financial Studies, 2020, 33(7): 2845-2897. [21] skarsdóttir M, Bravo C, Sarraute C, et al. The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics[J]. Applied Soft Computing, 2019, 74: 26-39. [22] Agarwal R R, Lin C C, Chen K T, et al. Predicting financial trouble using call data-On social capital, phone logs, and financial trouble[J]. PLoS One, 2018, 13(2): e0191863. [23] Bjrkegren D, Grissen D. Behavior revealed in mobile phone usage predicts credit repayment[J]. The World Bank Economic Review, 2020, 34(3): 618-634. [24] Ots H, Liiv I, Tur D. Mobile phone usage data for credit scoring[C]//Proceedings of 14th International Baltic Conference on Databases and Information Systems, Tallinn, Estonia, June 16-19, 2020. [25] 张懿玮, 高维和. 自我建构、文化差异和信用风险——来自互联网金融的经验证据[J]. 财经研究, 2020, 46(1): 34-48.Zhang Yiwei, Gao Weihe. Self-construals, cultural difference and credit risk: evidence from internet finance[J]. Journal of Finance and Economics, 2020, 46(1): 34-48. [26] Ma Lin, Zhao Xi, Zhou Zhili, et al. A new aspect on P2P online lending default prediction using meta-level phone usage data in China[J]. Decision Support Systems, 2018, 111: 60-71. [27] Lu Tian, Zhang Yingjie, Li Beibei. The value of alternative data in credit risk prediction: Evidence from a large field experiment[C]//Proceedings of Fortieth International Conference on Information Systems, Munich, Germany, December 15-18, 2019. [28] Niu Beibei, Ren Jinzhen, Li Xiaotao. Credit scoring using machine learning by combing social network information: Evidence from peer-to-peer lending[J]. Information, 2019, 10(12). [29] Shema A. Effective credit scoring using limited mobile phone data[C]//Proceedings of the Tenth International Conference on Information and Communication Technologies and Development,Ahmedabad, India, January 4-7, 2019. [30] Roa L, Correa-Bahnsen A, Suarez G, et al. Super-app behavioral patterns in credit risk models: Financial, statistical and regulatory implications[J]. Expert Systems with Applications, 2021, 169. [31] 陈丹.北京市外卖O2O型餐饮业消费者行为特征分析[D]. 兰州: 兰州大学,2019.Chen Dan. Analysis of consumer behavior characteristics of take-out O2O catering industry in Beijing[D]. Lanzhou: Lanzhou University,2019. [32] 廖理, 吉霖, 张伟强.借贷市场能准确识别学历的价值吗?——来自 P2P 平台的经验证据[J]. 金融研究, 2015(3): 146-159.Liao Li, Ji Lin, Zhang Weiqiang. Education and credit: Evidence from P2P lending platform[J]. Journal of Financial Research, 2015(3): 146-159. [33] 徐佳, 谭娅.中国家庭金融资产配置及动态调整[J]. 金融研究, 2016(12): 95-110.Xu Jia, Tan Ya. The dynamic adjustment of Chinese households’ financial asset allocation [J]. Journal of Financial Research, 2016(12): 95-110. [34] Blchlinger A, Leippold M. Economic benefit of powerful credit scoring[J]. Journal of Banking & Finance, 2006, 30(3): 851-873. [35] Stein R M. The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing[J]. Journal of Banking & Finance, 2005, 29(5): 1213-1236. [36] Bequé A, Coussement K, Gayler R, et al. Approaches for credit scorecard calibration: An empirical analysis[J]. Knowledge-Based Systems, 2017, 134: 213-227. [37] Fernandes G B, Artes R. Spatial dependence in credit risk and its improvement in credit scoring[J]. European Journal of Operational Research, 2016, 249(2): 517-524.
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