数据挖掘技术在银行客户提升中的应用研究

Research on the Application of Data Mining Technology in the Customers Improvement

随着金融行业竞争愈加剧烈,加之互联网金融的快速发展,银行业利差不断收窄、波动剧烈,使其正面临着全方位的挑战。如何更有效地进行精准营销在很大程度上决定银行是否能在激烈的竞争中脱颖而出。该研究将80%预处理后数据作为训练集,20%的数据用于验证集,利用数据挖掘技术中的Logistic回归和XGBoost两种客户提升模型分别对存量客户数据进行了比对分析。通过对比两种模型的ROC和Lift曲线后发现XGBoost模型提升客户数量更多、预测准确率较高。

With the fiercer competition in the financial sector and the rapid development of Internet finance, the banking industry isfacing comprehensive challenges due to the narrowing and drastic fluctuation of interest rate spreads. How to carry out precisionmarketing effectively determines whether it can stand out in the fierce competition. In this study, 80% of the pre-processed datawas taken as a training data, and 20% of the data was used for a validation set. Through data mining techniques, two customer improvement models including Logistic regression and XGBoost were used for comparative analysis of the existing customer data. Bycomparing the ROC and Lift curves of these two models, it was found that the XGBoost model can simultaneously improve the number of the customers and the accuracy of prediction.