Investigating customer churn in banking:a machine learning approach and visualization app for data science and management  

作　　者：Pahul Preet Singh Fahim Islam Anik Rahul Senapati Arnav Sinha Nazmus Sakib Eklas Hossain 

机构地区：[1]Institute for Artificial Intelligence and Data Science,University at Buffalo,The State University of New York,Amherst,14068,United States [2]Department of Mechanical Engineering,Khulna University of Engineering&Technology,Khulna,9203,Bangladesh [3]Department of Information Technology,Kennesaw State University,Marietta,30067,United States [4]Department of Electrical and Computer Engineering,Boise State University,Boise,83725,United States

出　　处：《Data Science and Management》2024年第1期7-16,共10页数据科学与管理（英文）

摘　　要：Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the bank.Therefore,customer retention is essential in today’s extremely competitive banking market.Additionally,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele.These factors make reducing client attrition a crucial step that banks must pursue.In our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers.We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics.In addition,we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis.Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.

关 键 词：Bank customer attrition Churn prediction Machine learning XGboost Random forest 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]