长尾需求下P2P网络借贷平台的职责、工具与手段——基于“拍拍贷”的微观借贷证据
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摘要
从平台风控的职责、工具和手段3个层面提出理论假设,并通过"拍拍贷"的微观借贷数据进行检验,结果表明:基于风险甄别的"信用背书"是P2P平台职责所在;基于大数据的风控系统是P2P平台的重要甄别工具,其信用评级能够解释"直观信息"未能表达的违约风险;借款人"行为轨迹"分析是平台风控的有效手段。
This paper puts forward theoretical hypotheses from the level of responsibilities,tools and means of platform. The empirical results show that: Firstly,the " credit endorsement" based on risk screening is the responsibility of P2 P platform.Secondly,the risk control system based on big data is an important screening tool for P2 P platform,and its credit rating can explain the default risk which " intuitive information" fails to express. Finally,the analysis of " track of behavior" of borrowers is an effective means of risk control.
This paper puts forward theoretical hypotheses from the level of responsibilities,tools and means of platform. The empirical results show that: Firstly,the " credit endorsement" based on risk screening is the responsibility of P2 P platform.Secondly,the risk control system based on big data is an important screening tool for P2 P platform,and its credit rating can explain the default risk which " intuitive information" fails to express. Finally,the analysis of " track of behavior" of borrowers is an effective means of risk control.
引文
[1] Yum H,Lee B,Chae M. From the Wisdom of Crowds to My Own Judgment in Microfinance Through Online Peer-to-peer Lending Platforms[J]. Electronic Commerce Research&Applications,2012,11(5):469-483.
[2] J Yannis Bakos. Reducing Buyer Search Costs:Implications for Electronic Marketplaces[J]. Management Science,1997,43(12).
[3] Pope D G,Sydnor J R. What's in a Picture?:Evidence of Discrimination from Prosper. com[J]. Journal of Human Resources,2011,46(1):53-92.
[4]范超,王磊,解明明.新经济业态P2P网络借贷的风险甄别研究[J].统计研究,2017(2):33-43.
[5] Chen X,Zhou L,Wan D. Group Social Capital and Lending Outcomes in the Financial Credit Market:An Empirical Study of Online Peer-to-peer Lending[J]. Electronic Commerce Research&Applications,2016,15(C):1-13.
[6]缪莲英,陈金龙. P2P网络借贷中社会资本对借款者违约风险的影响——以Prosper为例[J].金融论坛,2014(3):9-15.
[7] Rajkamal Iyer,Asim Ijaz Khwaja,Erzo F P,et al. Screening Peers Softly:Inferring the Quality of Small Borrowers[J]. Management Science,2015,62(6):1554-1577.
[8] Lin M,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.
[9]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,S1:143-155.
[10] Lin M,Viswanathan S. Home Bias in Online Investments:An Empirical Study of an Online Crowdfunding Market[J]. Social Science Electronic Publishing,2016,62.
[11]廖理,李梦然,王正位.聪明的投资者:非完全市场化利率与风险识别——来自P2P网络借贷的证据[J].经济研究,2014(7):125-137.
[12] Emekter R,Tu Y,Jirasakuldech B,et al. Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer(P2P)Lending[J].Applied Economics,2015,47(1):54-70.
[13]廖理,李梦然,王正位.中国互联网金融的地域歧视研究[J].数量经济技术经济研究,2014(5):54-70.
[14]姚国庆.征信体系的作用与效率——基于信息不对称随机有限重复博弈的分析[J].南开经济研究,2012(1):19-32.
[15]谢仲庆,刘晓芬.中国信用体系:模式构建及路径选择[J].上海金融,2014(7):63-66.
[16]陈文. P2P向死而生——科技投行赢得大未来[M].机械工业出版社,2016. 53.
(1)该方法参考了lyer(2015)等人的研究,将ROC曲线的真阳性率(TPR)看作是正确地拒绝高风险借款标的的概率,而假阳性率(FPR)看作是错误地拒绝低风险借款标的的概率。曲线与y=x(45°线)重合时,AUC=0.5,此时模型没有任何诊断力。曲线越向左上方凸起,AUC越接近于1,诊断效果越好。
[2] J Yannis Bakos. Reducing Buyer Search Costs:Implications for Electronic Marketplaces[J]. Management Science,1997,43(12).
[3] Pope D G,Sydnor J R. What's in a Picture?:Evidence of Discrimination from Prosper. com[J]. Journal of Human Resources,2011,46(1):53-92.
[4]范超,王磊,解明明.新经济业态P2P网络借贷的风险甄别研究[J].统计研究,2017(2):33-43.
[5] Chen X,Zhou L,Wan D. Group Social Capital and Lending Outcomes in the Financial Credit Market:An Empirical Study of Online Peer-to-peer Lending[J]. Electronic Commerce Research&Applications,2016,15(C):1-13.
[6]缪莲英,陈金龙. P2P网络借贷中社会资本对借款者违约风险的影响——以Prosper为例[J].金融论坛,2014(3):9-15.
[7] Rajkamal Iyer,Asim Ijaz Khwaja,Erzo F P,et al. Screening Peers Softly:Inferring the Quality of Small Borrowers[J]. Management Science,2015,62(6):1554-1577.
[8] Lin M,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.
[9]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,S1:143-155.
[10] Lin M,Viswanathan S. Home Bias in Online Investments:An Empirical Study of an Online Crowdfunding Market[J]. Social Science Electronic Publishing,2016,62.
[11]廖理,李梦然,王正位.聪明的投资者:非完全市场化利率与风险识别——来自P2P网络借贷的证据[J].经济研究,2014(7):125-137.
[12] Emekter R,Tu Y,Jirasakuldech B,et al. Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer(P2P)Lending[J].Applied Economics,2015,47(1):54-70.
[13]廖理,李梦然,王正位.中国互联网金融的地域歧视研究[J].数量经济技术经济研究,2014(5):54-70.
[14]姚国庆.征信体系的作用与效率——基于信息不对称随机有限重复博弈的分析[J].南开经济研究,2012(1):19-32.
[15]谢仲庆,刘晓芬.中国信用体系:模式构建及路径选择[J].上海金融,2014(7):63-66.
[16]陈文. P2P向死而生——科技投行赢得大未来[M].机械工业出版社,2016. 53.
(1)该方法参考了lyer(2015)等人的研究,将ROC曲线的真阳性率(TPR)看作是正确地拒绝高风险借款标的的概率,而假阳性率(FPR)看作是错误地拒绝低风险借款标的的概率。曲线与y=x(45°线)重合时,AUC=0.5,此时模型没有任何诊断力。曲线越向左上方凸起,AUC越接近于1,诊断效果越好。