基于机器学习的银行潜在客户挖掘方法研究与设计  

Research and Design of Bank Potential Customer Mining Method Based on Machine Learning

作　　者：王蒙 WANG Meng(Huaxia Bank Changchun Branch,Changchun Jilin 130000)

机构地区：[1]华夏银行长春分行,吉林长春130000

出　　处：《软件》2025年第2期153-155,共3页Software

摘　　要：为了更加深入地归纳整理商业银行客户办理业务规律,强化业务与客户的关系,本文基于Kaggle平台某商业银行某次贷款营销活动客户数据,利用机器学习Apriori算法,对个贷、证券等客户关联规则进行挖掘,并通过XGBoost模型预测可能办理个贷业务的潜在客户,实现高精确度、高准确率预测。测试结果证明,本文方法具有很好的潜在个贷客户挖掘性能。In order to further summarize and organize the business rules of commercial bank customers and strengthen the relationship between business and customers,this article is based on customer data from a loan marketing activity of a commercial bank on the Kaggle platform.Using machine learning Apriori algorithm,the association rules of personal loans,securities and other customers are mined,and the XG Boost model is used to predict potential customers who may handle personal loan business,achieving high-precision and high-accurate prediction.The test results prove that the method proposed in this paper has good potential for mining personal loan customers.

关 键 词：机器学习 商业银行 潜在客户 关联规则 客户预测 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]