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Credit card data from a real Chinese bank is used, and four groups of variables—customer information, card information, risk information, and transaction activity—are examined. The study selects a subset of variables based on correlation and economic meaning, rather than using all 135 variables directly. A misclassification cost metric considers the two error types and economic sense to evaluate model suitability, and experimental results show regression slightly outperforms the decision tree.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/credit-card-churn-forecasting-by-logistic-regression-and-decision-tree/606484/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/credit-card-churn-forecasting-by-logistic-regression-and-decision-tree/606484.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-10-16",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What models are used for credit card churn prediction?","Question",{"text":112,"@type":113},"The study applies logistic regression and a decision tree to build churn prediction models from credit card data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are input variables selected for the model?",{"text":117,"@type":113},"Instead of using all 135 variables, the paper examines four variable categories and selects certain variables using both correlation and economic sense.",{"name":119,"@type":110,"acceptedAnswer":120},"How is model performance evaluated beyond accuracy?",{"text":121,"@type":113},"The paper designs a misclassification cost measurement that accounts for two types of errors and economic considerations, making it more suitable for evaluating churn prediction.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},606484,1792274036,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","| Credit card churn forecasting by logistic regression and decision tree Guangli Nie a,e, Wei Rowe c, 1, Lingling Zhang a,b, Yingjie Tian a, Yong Shi a,d, ⇑\u003Cbr>a Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China\u003Cbr>b Management School, Graduate University of Chinese Academy of Sciences, Beijing 100190, China c Department of Finance, Banking & Law, University of Nebraska at Omaha, NE 68182-0048, USA d College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA e Postdoctoral Programme of Agricultural Bank of China, Beijing 100005, China |  |  |\n| --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |\n| Keywords:\u003Cbr>Credit card Customer churn Logistic regression Decision tree Data mining |  | In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. The paper analyzesa process of dealing with variables when data is obtained from a database instead of a survey. Instead of considering the all 135 variables into the model directly, it selects the certain variables from the perspective of not only correlation but also economic sense. In addition to the accuracy of analytic results, the paper designs a misclassification cost measurement by taking the two types error and the economic sense into account, which is more suitable to evaluate the credit card churn prediction model. The algorithms used in this study include logistic regression and decision tree which are proven mature and powerful classification algorithms. The test result shows that regression performs a little better than decision tree.\u003Cbr>􀀂 2011 Elsevier Ltd. All rights reserved. |\n\n1. Introduction  \nData mining refers to discover knowledge from a large amount of data. In this paper, we discuss the application of data mining including logistic regression and decision tree to predict the churn of credit card users. The banks can take corresponding actions to retain the customers according to the suggestion of the models.  \nWith today’s cost-cutting and intensive competitive pressure, more companies start to focus on Customer Relationship Management (CRM). The unknown future behaviors of the customers are quite important to CRM. Hence, it is of crucial importance to detect the customers’ future decision then the company can take corresponding actions early (Glady, Baesens, & Croux, 2008). The customers who stop using the company’s products are usually called churners. Finding the churners can help companies retain their customers. Gustafsson,Johnson, and Roos(2005)studied telecommunication services to examine the effects of customer satisfaction and behavior on customer retention. Results indicated a need for CRM managers to more accurately determine customer satisfaction in order to reduce customer churn.  \nOne of the major reasons for this is that it costs less to retain existing customers than to acquire new customers (Roberts, 2000).  \n⇑ Corresponding author at: Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, China. Tel./fax: +86 10 82680698.  \nE-mail addresses: [sdungl@163.com](sdungl@163.com) (G. Nie), [wrowe@mail.unomaha.edu](wrowe@mail.unomaha.edu)[ ](wrowe@mail.unomaha.edu)(W. Rowe), [zhangll@gucas.ac.cn](zhangll@gucas.ac.cn) (L. Zhang), [tianyingjie1213@163.com](tianyingjie1213@163.com) (Y. Tian), [yshi@gucas.ac.cn](yshi@gucas.ac.cn) (Y. Shi).  \n1 Tel.: +1 402 554 2812.  \n0957-4174/$ -see front matter 􀀂 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.06.028  \nIt costs up to five times as much to make a sale to a new customer as it does to make an additional sale to an existing customer (Dixon, 1999; Floyd, 2000; Slater & Narver, 2000). And, it is becoming more evident that the only way to remain a le","cbCaicUtgibpRHpv","https://ap.wps.com/l/cbCaicUtgibpRHpv","pdf",378910,13,"English","# Introduction\n## Customer relationship management and churn\n## Data mining for churn prediction\n## Importance of customer retention\n## Credit card market background in China","[{\"question\":\"What models are used for credit card churn prediction?\",\"answer\":\"The study applies logistic regression and a decision tree to build churn prediction models from credit card data.\"},{\"question\":\"How are input variables selected for the model?\",\"answer\":\"Instead of using all 135 variables, the paper examines four variable categories and selects certain variables using both correlation and economic sense.\"},{\"question\":\"How is model performance evaluated beyond accuracy?\",\"answer\":\"The paper designs a misclassification cost measurement that accounts for two types of errors and economic considerations, making it more suitable for evaluating churn prediction.\"}]","Credit card churn forecasting by logistic regression and decision tree | PDF",1792184535,33]