基于神经网络的银行长期存款客户预测研究  

Research on Bank Long‒Term Customer Deposit Prediction Based on Neural Network

作　　者：于春悦 曹宇 程旭 YU Chunyue;CAO Yu;CHENG Xu(School of Artificial Intelligence and Software,Liaoning Petrochemical University,Fushun Liaoning,113001,China)

机构地区：[1]辽宁石油化工大学人工智能与软件学院,辽宁抚顺113001

出　　处：《辽宁石油化工大学学报》2023年第5期91-96,共6页Journal of Liaoning Petrochemical University

基　　金：辽宁省社会科学规划基金重点项目(L19AGL010)。

摘　　要：因客户数据量庞大、各种理财产品的兴起和疫情的短期冲击,银行面临的压力越来越大,使用数据分析和预测方法能够更大程度提升银行的业务量。使用传统的分类树模型无法根据客户信息对可能长期存款的客户做出更加精准的预测,从而导致无法对客户进行精准营销。因此,提出了一种分三层搭建的神经网络模型。通过实验,对葡萄岛银行机构客户数据进行预测,并和传统的决策树模型、随机森林模型、Adaboost模型、XGBoost模型的预测结果进行了对比。结果表明,相比于其他四种模型,神经网络模型预测效果更好,模型评估AUC达到了0.9777,准确率达到了99.06%。Due to the huge amount of the customers′data the rise of various financial products and the short‒term impact of the epidemic,banks are facing with increasing pressure resulting in the business volume declined sharply.The traditional classification tree model can not make more accurate prediction of long‒term deposits and carry out accurate marketing to customers according to customer information.Therefore,this paper proposes a three‒layer neural network model.Through the experiment,the customer data of grape Island banking institutions are predicted,and compared with the prediction results of a traditional decision tree,random forest model,AdaBoost model and XGBoost model.The experiment shows that compared with the other four models,the neural network model has a better effect of prediction,the model evaluation AUC reaches 0.9777 and the accuracy reaches 99.06%.

关 键 词：神经网络 决策树 随机森林 Adaboost模型 XGBoost模型 精准营销 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]