[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128545-en":3,"doc-seo-128545-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128545,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Investigating Customer Churn in Banking - A Machine Learning Approach and Visualization App for Data Science and Management","Customer attrition in banking occurs when clients stop using a bank’s products and services and later end their relationship, making retention critical in a highly competitive market. The study examines banking data to predict which users are likely to churn and cease becoming paying customers. Multiple machine learning models are used for comparative analysis across evaluation metrics, and a Data Visualization RShiny app is developed to support churn analysis. Insights help identify attrition trends so banks can retain at-risk customers.","Boise State University  \nScholarWorks  \n\n| Electrical and Computer Engineering Faculty Publications and Presentations | Department of Electrical and Computer Engineering |\n| --- | --- |\n\n3-2024  \nInvestigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management  \nPahul Preet Singh  \nState University of New York, University at Buffalo  \nFahim Islam Anik  \nKhulna University of Engineering & Technology  \nRahul Senapati  \nState University of New York, University at Buffalo  \nArnav Sinha  \nState University of New York, University at Buffalo  \nNazmus Sakib  \nKennesaw State University  \nSee next page for additional authors  \nAuthors  \nPahul Preet Singh, Fahim Islam Anik, Rahul Senapati, Arnav Sinha, Nazmus Sakib, and Eklas Hossain  \nThis article is available at ScholarWorks: [https://scholarworks.boisestate.edu/electrical_facpubs/563](https://scholarworks.boisestate.edu/electrical_facpubs/563)  \nData Science and Management 7 (2024) 7–16  \nContents lists available at ScienceDirect  \nData Science and Management  \njournal [homepage:](homepage: www.keaipublishing.com/en/journals/data-science-and-management)[ www.keaipublishing.com/en/journals/data-science-and-management](homepage: www.keaipublishing.com/en/journals/data-science-and-management)  \nResearch article  \nInvestigating customer churn in banking: A machine learning approach and visualization app for data science and management  \nPahul Preet Singh a, Fahim Islam Anikb, Rahul Senapati a, Arnav Sinha a, Nazmus Sakib c, *, Eklas Hossain d  \na Institute for Artificial Intelligence and Data Science, University at Buffalo, The State University of New York, United States b Department of Mechanical Engineering, Khulna University of Engineering & Technology, Bangladesh  \nc Department of Information Technology, Kennesaw State University, United States  \nd Department of Electrical and Computer Engineering, Boise State University, United States  \n\n| A R T I C L E I N F O |  | A B S T R A C T |\n| --- | --- | --- |\n| Keywords:\u003Cbr>Bank customer attrition Churn prediction Machine learning\u003Cbr>XG boost\u003Cbr>Random forest |  | 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. |\n\n1. Introduction  \nCustomer attrition, also known as customer churn, is the phenomenon where customers terminate their relationship with a business or organization. In the context of banking, customer attrition occurs when customers close their accounts or discontinue utilizing services of a particular bank. Effectively understanding and managing customer attrition are crucial for banks to maintain financial stability and safeguard their reputation. The financial impact of customer attrition on banks can be significant, resulting in potential revenue loss across various banking services. Consequently, establishing and nurturing long-term customer relationships is highly valuable for banks. By gaining insight into attrition patterns, banks can identify customers at risk of leaving and implement strategies to retain them. This","cbCaipwUrdDGki72","https://ap.wps.com/l/cbCaipwUrdDGki72","pdf",2542887,3,1,12,"English","en",105,"# Introduction\n## Customer attrition and its impact in banking\n## Predictive modeling with machine learning\n## Visualization support via RShiny app","[{\"question\":\"What problem does the research address in the banking context?\",\"answer\":\"The research addresses customer attrition (churn) in banking, where clients discontinue using a bank’s services and eventually end their relationship.\"},{\"question\":\"How is customer churn predicted in the proposed approach?\",\"answer\":\"The study analyzes bank data using multiple machine learning algorithms and compares them using different evaluation metrics.\"},{\"question\":\"What tool is provided to support churn analysis beyond modeling?\",\"answer\":\"A Data Visualization RShiny app is developed to help users analyze customer churn and interpret results for data science and management use.\"}]","Investigating Customer Churn in Banking - 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