Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

基于CRISP-DM的移动用户离网分析与预测

为了准确地识别高离网倾向的用户群,基于运营商的实际业务数据,通过运用数据挖掘中的CRISP-DM模型方法对移动用户的离网行为进行分析,并详细介绍了离网预测模型的建立过程。特别地,为了提高模型预测精度,将全量用户分为正常和非正常用户,对比分析这两类用户之间的差异并分别建模。经过数据实验证明了模型的有效性,能够达到实际商业应用的要求。

服务号码捆绑特征在离网预测系统中的应用

用户流失问题是电信运营商面临的亟待解决的问题,针对不同的场景,业界研究开发了多个用户离网预测系统。服务号码捆绑指用户在使用运营商服务期间,与银行、电商、便利店等第三方服务提供商通过绑定手机号产生联系。通过研究发现用户在服务存续期间普遍会绑定多种第三方服务提供商,这些商家会不定时给用户推送短信,当用户即将流失时,多数用户会逐渐取消这类服务的绑定。因此,服务号码捆绑特征对于离网用户的甄别起到了重要的作用。采用随机森林算法构建离网预测模型,利用逻辑回归算法对服务号码捆绑特征进行降维,并加入模型,进行离网用户分析,从而辅助决策者制订相应的客户维挽策略,降低客户离网率。实验结果表明,服务号码软捆绑特征能够提高系统的分析预测能力。

关于银行客户流失预测的数学建模分析

为研究国内银行客户流失情况,对某商业银行客户流失的真实数据进行了采集,利用数据分析系统软件(SAS),用四种比较常用的数学建模方式进行分析,并对分析的结果结合业务需求做了比较,最后选择逻辑回归模型进行了客户流失预测,并对银行客户保留问题提出了有针对性的对策建议。