文档介绍：融入软信息的P2P网络借贷违约预测方法
蒋翠清王睿雅丁勇
合肥工业大学管理学院
在P2P网络借贷屮,预测借款的违约概率是用户信用评价的关键,也是借贷平台与投资者关注的重点问题。由于P2P平台所获取的用户财务信息有限,P2P借款信用评价和违约预测面临新的挑战。本文结合P2P平台的信息特点,提出一种融入软信息的网络借款违约预测方法。首先利用主题模型抽取并量化文木软信息中的相关变量,进而分析不同软信息变量对借款违约的影响关系;其次,设计了一种两阶段的变量选择方法对软硬信息进行组合筛选;最后,引入随机森林算法构建融入软信息的违约预测模型,并结合P2P平台的真实数据进行实证分析。结果表明,在P2P借款的违约预测模型中融入有价值的软信息可以提高预测准确率。
关键词:
P2P借贷;违约预测;软信息;主题模型;变量选择;随机森林;
王睿雅(1992-),女(汉族),安徽合肥人,合肥工业大学管理学院,研宄生,研宄方位:大数据分析、信用评价,E-mail:******@163. com.
2016-07-06
基金:国家自然科学基金资助项目(71731005, 71571059)
The Default bined with Soft Informationin Online Peer-to-Peer Lending
JIANG Cui-qing WANG Rui-va DIGN Yong
School of Management, Hefei University of
Technology;
Abstract:
P2P lending is a new type of loan mode formed by the intersection of and traditional finance. It provides a more convenient loan platform and has been developing rapidly in China. However, the phenomenon of col lapse in P2P is getting worse as P2P loans is facing defaul t risk and bad debt losses seriously. Credit evaluation is an important basis for managing loan default risk and supporting lending decision. Compared with traditional loans, the financial data of borrowers collected by P2P platform is limited, which is also called the hard the information. However, there is lots of soft information generated during the loan application, such as loan description text, also involving some information about loans and borrowers. Therefore, a default prediction bined with soft informationfor P2P lending is proposed. Firstly, the soft information is categorized according to the characteristics of P2P,and the LDA topic model is used to quantify valuable factors in the text of soft information. Secondly, some regression analysis and contrast experiments are performed to test the effect of soft information on P2P default probability. Moreover, a two-stage method is designed to selecteffective variablesets for default modeling, and the default prediction model is constructed through the random forest (RF) method Fin