P2P网贷借款人信用风险预警研究
|
摘要
近年来,P2P网络借贷行业信用风险日益凸显,"倒闭潮""跑路潮"屡见不鲜,在造成出借人资金损失的同时,也影响了P2P网络借贷行业的健康发展。P2P网络借贷借款人信用风险的形成,一方面与网络借贷借款人违约有关,如借款人无力履约还款、借款人恶意违约;另一方面与网贷平台对借款人信用风险的防控能力较弱有关,如P2P网络借贷平台信贷管理流程不完善、未建立有效的P2P网络借贷借款人信用风险预警系统等。为更好地推动P2P网络借贷平台健康发展,对借款人信用风险早识别、早预警,并加以有效防范,可构建主要包括输入防范要素、风险预警、风险防范、预警结果输出四部分运行内容的P2P网络借贷借款人信用风险预警系统。该预警系统的运行可分为查找造成网络借贷借款人信用风险的各种原因、得出借款人当前信用风险状态、针对预警系统发出的预警信号采取适当措施防范借款人信用风险、通过危机处理产生成功或失败两种不同结果等四个步骤。而为保障P2P网络借贷借款人信用风险预警系统的顺利运行,还需要加强借款人贷后风险监控,建立借款人信用风险预警信息库,构建复合型、专家型信用风险防范管理人才队伍。
In recent years,the credit risk of the P2P online lending industry has become increasingly prominent. The"downturn tide"and the"running tide"are common occurrences,causing the loss of lenders' funds and affecting the healthy development of the P2P online lending industry. On one hand,the causes of the credit risk of P2P online lending borrowers are related to borrower's default,including the borrower's inability to repay and the borrower's malicious default;on the other hand,they are related to the online lending platform's poor ability to prevent and control the credit risk of borrowers,including the imperfect credit management process of the P2P online lending platform,and the establishment of an effective P2P online lending borrower credit risk early warning system. To better promote the healthy development of P2P online lending,it is urgent for us to establish the early warning system to identify and prevent credit risk. Considering the borrower's credit risk involving in P2P network loan,we can construct an early warning system to control P2P network loan borrower's credit risk,which includes prevention factors,risk warning,risk prevention and early warning result output. The operation process of the early warning system includes four steps such as finding the credit risk of the network loan borrower,obtaining the current credit risk status of the borrower,taking appropriate precautionary measures against the early warning signal issued by the early warning system to prevent the credit risk of the borrower,and obtaining the result of"success"or"failure". In order to ensure the smooth operation of the credit risk early warning system for P2P online lending borrowers,we should also strengthen post-lending risk monitoring,establish a credit risk warning database,and cultivate more professionals with both comprehensive and special knowledge.
In recent years,the credit risk of the P2P online lending industry has become increasingly prominent. The"downturn tide"and the"running tide"are common occurrences,causing the loss of lenders' funds and affecting the healthy development of the P2P online lending industry. On one hand,the causes of the credit risk of P2P online lending borrowers are related to borrower's default,including the borrower's inability to repay and the borrower's malicious default;on the other hand,they are related to the online lending platform's poor ability to prevent and control the credit risk of borrowers,including the imperfect credit management process of the P2P online lending platform,and the establishment of an effective P2P online lending borrower credit risk early warning system. To better promote the healthy development of P2P online lending,it is urgent for us to establish the early warning system to identify and prevent credit risk. Considering the borrower's credit risk involving in P2P network loan,we can construct an early warning system to control P2P network loan borrower's credit risk,which includes prevention factors,risk warning,risk prevention and early warning result output. The operation process of the early warning system includes four steps such as finding the credit risk of the network loan borrower,obtaining the current credit risk status of the borrower,taking appropriate precautionary measures against the early warning signal issued by the early warning system to prevent the credit risk of the borrower,and obtaining the result of"success"or"failure". In order to ensure the smooth operation of the credit risk early warning system for P2P online lending borrowers,we should also strengthen post-lending risk monitoring,establish a credit risk warning database,and cultivate more professionals with both comprehensive and special knowledge.
引文
[1]李昕,戴一成.基于BP神经网络的P2P网贷借款人信用风险评估研究[J].武汉金融,2018(2):33-37.
[2]全颖,杨大光,郑策.基于逆向剪枝模型的P2P网络借贷平台进退分析[J].企业经济,2017(4):80-84.
[3]2017年P2P行业年报简报:问题平台数持续降低[EB/OL].(2018-01-01)[2018-09-10]. http://www. wdzj. com/news/yc/1714657.html.
[4]ABERER Z,DEPOTOVIC K.Managing trust in a peer-topeer information system[C].Proceedings of the ACM conference on information and knowledge management,USA,2001:310-317.
[5]KAMVAR S,SCHLOSSER M,GARCIA-MOLINA H.The eigentrust algorithm for reputation management in peer-topeers[C].Proceedings of ACM world wide web conference,Budapest,Hungary,2003:640-651.
[6]BERGER S C,GLEISNER F.Emergence of financial intermediaries in electronic markets:the case of online P2P lending[J].Business research,2009,2(1):39-65.
[7]HERZENSTEIN M,SONENSHEIN S,DHOLAKIA U M.Tell me a good story and I may lend you money:the role of narratives in peer-to-peer lending decisions[J].Journal of marketing research,2011(48):138-149.
[8]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.
[9]程识.中国互联网信用体系建设的策略研究——基于中国人民银行征信平台的视角[J].征信,2010(3):9-12.
[10]高汉.互联网金融的发展及其法制监管[J].中州学刊,2014(2):57-61.
[11]刘芸,朱瑞博.互联网金融、小微企业融资与征信体系深化[J].征信,2014(2):31-35.
[12]夏志琼.互联网金融信用体系建设的难点与对策[J].国际金融,2014(10):72-75.
[13]牛润盛.互联网金融背景下的征信模式选择[J].海淀金融,2014(7):53-61.
[14]曾菊凡.互联网金融与征信业的互动耦合发展机理研究[J].中国人口·资源与环境,2015(S1):617-619.
[15]于洁,冯冰娜,樊博.协同治理视角下互联网金融征信治理问题研究[J].电子政务,2015(11):15-24.
[16]王伟.中国互联网金融征信的二维思考——基于经济学与法学视角[J].哈尔滨工业大学学报(社会科学版),2017(6):50-55.
[17]赵园园.互联网征信中个人信息保护制度的审视与反思[J].广东社会科学,2017(3):212-220.
[18]网贷借款人数达近20个月最大环比降幅为8.55%[EB/OL].(2018-01-01)[2018-09-10].https://bbs.wdzj. com/thread-1062315-1-1.html.
[19]孟娜娜,粟勤.软信息对网贷平台借款人的借款获得与违约风险的影响——来自“人人贷”的经验证据[J].中国流通经济,2018(3):85-94.
[20]全颖,杨大光.互联网金融发展、支付货币电子化及对货币供给的影响[J].中国流通经济,2016(7):122-128.
[2]全颖,杨大光,郑策.基于逆向剪枝模型的P2P网络借贷平台进退分析[J].企业经济,2017(4):80-84.
[3]2017年P2P行业年报简报:问题平台数持续降低[EB/OL].(2018-01-01)[2018-09-10]. http://www. wdzj. com/news/yc/1714657.html.
[4]ABERER Z,DEPOTOVIC K.Managing trust in a peer-topeer information system[C].Proceedings of the ACM conference on information and knowledge management,USA,2001:310-317.
[5]KAMVAR S,SCHLOSSER M,GARCIA-MOLINA H.The eigentrust algorithm for reputation management in peer-topeers[C].Proceedings of ACM world wide web conference,Budapest,Hungary,2003:640-651.
[6]BERGER S C,GLEISNER F.Emergence of financial intermediaries in electronic markets:the case of online P2P lending[J].Business research,2009,2(1):39-65.
[7]HERZENSTEIN M,SONENSHEIN S,DHOLAKIA U M.Tell me a good story and I may lend you money:the role of narratives in peer-to-peer lending decisions[J].Journal of marketing research,2011(48):138-149.
[8]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.
[9]程识.中国互联网信用体系建设的策略研究——基于中国人民银行征信平台的视角[J].征信,2010(3):9-12.
[10]高汉.互联网金融的发展及其法制监管[J].中州学刊,2014(2):57-61.
[11]刘芸,朱瑞博.互联网金融、小微企业融资与征信体系深化[J].征信,2014(2):31-35.
[12]夏志琼.互联网金融信用体系建设的难点与对策[J].国际金融,2014(10):72-75.
[13]牛润盛.互联网金融背景下的征信模式选择[J].海淀金融,2014(7):53-61.
[14]曾菊凡.互联网金融与征信业的互动耦合发展机理研究[J].中国人口·资源与环境,2015(S1):617-619.
[15]于洁,冯冰娜,樊博.协同治理视角下互联网金融征信治理问题研究[J].电子政务,2015(11):15-24.
[16]王伟.中国互联网金融征信的二维思考——基于经济学与法学视角[J].哈尔滨工业大学学报(社会科学版),2017(6):50-55.
[17]赵园园.互联网征信中个人信息保护制度的审视与反思[J].广东社会科学,2017(3):212-220.
[18]网贷借款人数达近20个月最大环比降幅为8.55%[EB/OL].(2018-01-01)[2018-09-10].https://bbs.wdzj. com/thread-1062315-1-1.html.
[19]孟娜娜,粟勤.软信息对网贷平台借款人的借款获得与违约风险的影响——来自“人人贷”的经验证据[J].中国流通经济,2018(3):85-94.
[20]全颖,杨大光.互联网金融发展、支付货币电子化及对货币供给的影响[J].中国流通经济,2016(7):122-128.