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信用卡违约预测模型分析以及影响因素探究
Study on Analysis and Influence Factors of Credit Card Default Prediction Model

DOI: 10.12677/SA.2016.53026, PP. 263-275

Keywords: 信用卡,信用风险,随机森林,Lasso-Logistic模型
Credit Card
, Credit Risk, Random Forest, Lasso-Logistic Model

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Abstract:

信用卡对于银行来说是高收益和高风险并存的业务,伴随信用卡业务发展的是各大银行都在利用网络和移动端的数据来建立客户的信用评分系统。如何从客户所填的资料里对客户进行信用评估、如何鉴别所填资料的真假性及应该要求客户填什么类型的资料等对银行来说是至关重要的。本文基于2005年台湾信用卡客户数据,建立Lasso-Logistic及随机森林模型来探索影响客户信用的关键因素,包括个体特征及某些客观特征,通过比较模型的预测准确度以及F得分等指标来选择预测效果更优的模型对银行信用卡违约进行预测分析。信用卡违约预测模型的建立以及影响客户信用的关键因素的探索,对于银行选择客户和设计资料填写具有重要的指导价值,并且能够为信贷决策提供一定的理论支持,具有很强的理论和现实意义。
Credit cards are a bank business in which high income and heavy risk coexist. Along with the de-velopment of the credit card business, banks are using the Internet and mobile data to establish customer credit rating system. How to evaluate customer credit from the information that cus-tomers fill in, and how to identify the information true or false, and what type of information that customers are asked to fill are crucial for banks. Based on the credit card customer data of 2005 in Taiwan, this article established Lasso-Logistic model and random forest model to explore the key factors which effect customer credit, including individual characteristics and some objective cha-racteristics. Through comparing the prediction accuracy of the model and F score index, we selected the model of better prediction effect to forecast the bank credit card defaults. The establishment of the credit card default prediction model and the exploration of the key factors influencing the customer credit not only have a important guidance value for banks to choose customers and design data, but also can provide certain theoretical support for the credit decisions. In addition, it has a strong theoretical and practical significance.

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