手机App列表信息在信用风险评价中的应用——基于互联网借贷平台的实证研究  

Application of Mobile App List in Evaluating Borrowers’ Credit Risk——An Empirical Analysis of an Online Lending Platform

作　　者：郭伟栋 周志中 乾春涛 GUO Wei-dong;ZHOU Zach Zhi-zhong;QIAN Chun-tao(Antai College of Economics and Management,Shanghai Jiao Tong University,Shanghai 200030,China;School of Economics and Management,Tongji University,Shanghai 200092,China;Shuhe Group,Shanghai 201203,China)

机构地区：[1]上海交通大学安泰经济与管理学院,上海200030 [2]同济大学经济与管理学院,上海200092 [3]上海数禾信息科技有限公司,上海201203

出　　处：《中国管理科学》2022年第12期96-107,共12页Chinese Journal of Management Science

基　　金：国家自然科学基金资助项目(71771148,71421002)。

摘　　要：随着互联网的发展和智能手机的普及,用户手机数据被用来评估借款人的信用风险,使用到的数据有通讯记录、短信息接发、移动轨迹、用户行为数据等,而本文研究了手机上所安装的App列表和借款人信用风险之间的关系。通过对某大型互联网借贷平台上的个人借贷数据以及借款人手机上安装的App列表数据的分析发现,手机上安装的App和借款人的信用状况存在关联关系。安装生活类、金融类和买房买车类App的借款人比没有安装这些App的借款人信用风险低;其中,记账类App、外卖类App、股票类App和买房类App对借款人的信用风险有较强的识别能力。把手机App列表信息加入信用风险评价模型之后,信用风险评价模型的区分能力得到显著提高。With the development of the Internet and popularity of smart phones,data related to the use of mobile devices are used to study the default risk of borrowers,including communication records,short message receiving,mobile track and user behavior data.Data from a large online lending platform are adopted to study whether mobile Apps is related to the credit risk.Three types of Apps are analyzed,namely lifestyle Apps,financial Apps,and property Apps.Personal accounting Apps,takeout Apps and workout Apps are categorized as lifestyle Apps;fund Apps,stock Apps and future Apps are categorized as financial Apps;and car-buying Apps and house-buying Apps are categorized as property Apps.The empirical results show that the usage of these Apps is related to borrowers’ credit risk.Borrowers who install lifestyle Apps,financial Apps,and property Apps have significantly lower credit risk than those who do not install these Apps.In particular,accounting Apps,takeout Apps,stock Apps,and house-buying Apps are good indicators to identify borrowers with good credit.Mobile App usage information is incorporated in the credit risk prediction model,and AUC(Area Under the receiver operating characteristic Curve) is used to measure the prediction performance.The prediction performance is significantly improved by 1% after mobile App usage is incorporated in the credit risk prediction model,demonstrating that the mobile app information can enhance the discriminative ability of the prediction model and help to increase the revenue of financial institutions.

关 键 词：手机App 信用风险 违约预测 互联网借贷 金融科技 

分 类 号：F832.4[经济管理—金融学]