行为轨迹及社交信息能预测用户是否再借款吗?——基于网络借贷的实证分析

Can Behavioral Track and Social Information Predict Whether Online Lending Users will Refinance? An Empirical Analysis Based on Online Lending

网络借贷具有高频次、反复借贷的特点,用户往往具有再次借款的需求,利用网络平台累积的各种信息,特别是用户的"行为轨迹"和"社会交往"数据,对平台上具有"再次借款"可能的优质用户进行挖掘,不仅能提高平台的运营效率,也能促使网络借贷市场平稳长远的发展。文章采用XGBoost算法,利用网络借贷平台上2.6万个用户68万多条数据,首创性地建立了用户"是否再次借款"的预测模型,并对用户关键特征进行可视化分析。主要结论为:网络借贷中预测用户是否申请再借款,用户提供的"硬信息"(个人基本信息)已不具有信号揭示作用,用户在平台上的借贷和消费的"行为轨迹"信息以及"社会交往"信息,更具有信任信号的揭示作用。如果在平台上积极维护个人信息、保持良好的还款记录、维持良好网络社交的用户,再次申请借款的可能性就很高。

Online lending has the characteristics of 'high frequency' and 'repeated borrowing'. Users often have the demand of refinance, so how to use the information accumulated by the online platform, especially users’ 'Behavioral Track' data and 'Social Information' data,to dig the high quality and loyal users who have the demand of refinance is very important, which can not only improve the operational efficiency of the online lending platform,but also keep the online lending market stable in the long-term. In this paper,with the XGBoost method,we use the information of 26,000 users from the online lending platform to create a refinance forecasting model. The main conclusions are as follows:(1)To predict whether users refinance in online lending,'hard information'(or personal basic information) provided by users has no signal effect. 'Trajectory' information and 'social interaction' information are more revealing of the trust signal.(2)Users who actively maintain their personal information and keep good repayment records on the online lending platform are highly likely to apply for loans again.The closer the time when users last modified their personal information,the higher the frequency of user ID application and maximum monthly repayment,the more likely they are to apply for refinance and become loyal users on the online lending platform.(3)Users who follow more friends on the online lending platform,have good social capital and actively maintain their social network relationships,are more likely to apply for loans again. Borrowers build social capital through social network relations of the platform and reveal their own reputation signals to investors. Such users will often refinance and become high-quality customers of the online lending platform. Therefore,the online lending platform can make use of its Internet and big data advantages to strengthen borrowers’ constraint mechanism,enlarge their reputation mechanism,and reduce information asymmetry to improve the online transaction efficiency through data mining.