基于LASSO-SVM模型的银行定期存款电话营销预测

Abstract

定期存款一直以来都是银行的主要资金来源，而电话营销也成为一种低成本，广受银行欢迎的营销模式。因此，如何提高电话营销成功率成为银行急需解决的重要问题。其中，影响客户订购定期存款的因素复杂多样，而这些因素之间可能存在多重共线性，如果银行不加选择地引入众多影响因素来进行订购定期存款的预测，往往不能取得良好的预测效果，甚至产生错误的决策。在统计学习方法中，LASSO方法可以同时进行参数估计和变量选择，所以本文提出了基于LASSO与支持向量机的组合预测方法。同时，与SVM、神经网络、LASSO-神经网络方法的预测效果进行比较，验证了LASSO-支持向量机组合预测方法的拟合预测效果要优于另外三种预测方法。 Time deposits have always been the main source of funds for the bank, and the telephone mar-keting has become a low-cost marketing model, which is widely popular with the bank. Therefore, how to improve the success rate of telemarketing has become an important problem to solve. Among them, the factors that affect customers ordering deposits are complicated, which may have multicollinearity. If banks indiscriminately use many influence factors to predict deposits, they often cannot obtain good prediction effects, and even make the wrong decision. In the statistical learning methods, the LASSO method can be used to estimate parameters and select variables, so this paper presents a combination forecast method based on the LASSO and Support Vector Ma-chine (SVM). At the same time, compared with SVM, neural network, LASSO-neural network me-thods, we find that the effect of LASSO-SVM forecasting method is better than the other three kinds of forecasting methods.