近年来,我国信用卡发卡量呈下降趋势,信用卡行业正在告别过去激进的发展模式。随着休眠信用卡的不断增加和客户流失现象的加剧,信用卡行业面临着管理和运营的新压力。竞争激烈的市场环境下,银行为了争夺客户,可能会采取过度放宽授信额度等手段,这在一定程度上埋下了潜在的风险。因此,对信用卡流失进行预测和管理变得尤为重要。本论文采用逻辑回归、决策树以及随机森林三种机器学习模型进行预测信用卡客户的流失情况,帮助银行业更好地理解客户行为和需求,及时发现潜在的风险,采取相应的措施,提高客户保留率。通过深入研究信用卡流失背后的原因,银行可以更好地适应市场变化,提供更优质的金融服务,实现信用卡业务的良性发展。In recent years, the number of credit card issuance in China has shown a downward trend, and the credit card industry is bidding farewell to the past radical development model. With the continuous increase of dormant credit cards and the increasing customer loss phenomenon, the credit card industry is facing new pressure from management and operation. In the highly competitive market environment, banks may compete for customers to excessively relax credit lines, which lays down potential risks to some extent. Therefore, it is particularly important to predict and manage credit card loss. In this paper, three machine learning models of logical regression, decision tree and random forest are used to predict the loss of credit card customers, so as to help the banking industry better understand customer behavior and needs, find potential risks in time, and take corresponding measures to improve the customer retention rate. Through the in-depth study of the reasons behind the credit card loss, banks can better adapt to the market changes, provide better financial services, and realize the benign development of the credit card business.
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