浅析我国P2P网络借贷大数据风控体系构建的必要性
|
摘要
网贷为中小企业提供了融资渠道,也为进一步实现普惠金融目标做出了应有的贡献。网贷行业的发展引发了众多风险问题,市场中存在征信体系不完善、市场对接机制不集中、大数据系统缺失以及运用的有效性难保障等问题,加强和完善基于互联网大数据的风控体系十分必要。国外实践证明大数据风控贯穿于网贷的各个环节,大数据风控能有效防范金融风险。为保障网贷行业的可持续发展,应运用大数据思维、搭建大数据征信机构、真实的执行大数据风控和构建数据化风控模型等。
China's P2P lending industry has provided financing channels for small and medium-sized enterprises,and mading contributions to further achieve the goal of inclusive finance. The development of P2P lending industry has caused many risk problems. It is necessary to strengthen and improve the wind control system based on big data on the Internet,because of the problems such as imperfect credit system,uncentralized market interface mechanism,lacking of large data system,and difficulty in ensuring the effectiveness of application. Foreign practice has proved that large data wind control runs through all aspects of online lending,and large data wind control can effectively prevent financial risks. In order to ensure the sustainable development of the P2P lending industry,we should have and use big data thinking,building big data credit agencies,implementing real big data wind control and building data wind control model.
China's P2P lending industry has provided financing channels for small and medium-sized enterprises,and mading contributions to further achieve the goal of inclusive finance. The development of P2P lending industry has caused many risk problems. It is necessary to strengthen and improve the wind control system based on big data on the Internet,because of the problems such as imperfect credit system,uncentralized market interface mechanism,lacking of large data system,and difficulty in ensuring the effectiveness of application. Foreign practice has proved that large data wind control runs through all aspects of online lending,and large data wind control can effectively prevent financial risks. In order to ensure the sustainable development of the P2P lending industry,we should have and use big data thinking,building big data credit agencies,implementing real big data wind control and building data wind control model.
引文
[1]蔡丰庆,郭春松,陈诣之.大数据思维在金融学研究中的运用[J].经济学动态,2015(3):104-114.
[2]巴曙松,候畅等.大数据风控的现状、问题及优化路径[J].金融理论与实践,2016(2):23-26.
[3]朱大磊.大数据风控助力普惠金融[J].中国金融,2017(11):56-57.
[4]黎宁.大数据在互联网金融企业风险控制下应用研究[D].广西大学,2018(6).
[5]黄硕等.基于大数据下互联网金融的风控研究[J].农村经济与科技,2019(1):110-111.
[6]王冬吾.国外P2P网贷大数据风控管理与经验借鉴[J].西南金融,2019(2):90-96.
[7]陆登强.立足金融科技,大数据智能风控助力普惠金融[J].中国金融电脑,2018(8).
[2]巴曙松,候畅等.大数据风控的现状、问题及优化路径[J].金融理论与实践,2016(2):23-26.
[3]朱大磊.大数据风控助力普惠金融[J].中国金融,2017(11):56-57.
[4]黎宁.大数据在互联网金融企业风险控制下应用研究[D].广西大学,2018(6).
[5]黄硕等.基于大数据下互联网金融的风控研究[J].农村经济与科技,2019(1):110-111.
[6]王冬吾.国外P2P网贷大数据风控管理与经验借鉴[J].西南金融,2019(2):90-96.
[7]陆登强.立足金融科技,大数据智能风控助力普惠金融[J].中国金融电脑,2018(8).