[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-606622-105":59,"doc-detail-606622-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-classification-application-for-using-learning-methods-in-bank-costumers-portfolio-churn","A classification application for using learning methods in bank costumer's portfolio churn","","Customer loss and gain studies are essential for maintaining customer-company loyalty and controlling corporate financial flows. This work focuses on banking customer churn by classifying customer groups using a dataset and multiple machine learning models. Data imbalance is analyzed because it can harm success metrics. Model performance is assessed via F1-score, precision, recall, and accuracy comparisons. Results show that the proposed random oversampling (ROS)-voting ensemble (random forest and gradient boosting) achieves the strongest classification and prediction, with 95% accuracy.",{"@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/a-classification-application-for-using-learning-methods-in-bank-costumers-portfolio-churn/606622/",{"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/a-classification-application-for-using-learning-methods-in-bank-costumers-portfolio-churn/606622.png","ImageObject",300,407,{"name":92,"@type":93},"Mabel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-10-16",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in the banking sector?","Question",{"text":112,"@type":113},"The study addresses customer-group classification for portfolio churn, aiming to support customer loyalty and financial-flow control.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which evaluation metrics are used to compare models?",{"text":117,"@type":113},"The models are compared using F1-score, precision, recall, and accuracy.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed approach handle imbalanced data?",{"text":121,"@type":113},"It applies random oversampling (ROS) and then uses a voting ensemble combining random forest and gradient boosting models.","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},606622,1792439248,{"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":81,"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},7971474920318,"https://ap-avatar.wpscdn.com/avatar/74000ee537e6b0ca360?x-image-process=image/resize,m_fixed,w_180,h_180&k=1788430171632181674","Received: 17 March 2023 Revised: 23 August 2023 Accepted: 12 September 2023  \nDOI: 10.1002/for.3038  \nRESE ARCH ARTICL E  \nA classification application for using learning methods in bank costumer's portfolio churn  \nMurat Simsek 1  | Iclal Cetin Tas 2  \n1Artificial Intelligence Engineering Department, Ostim Technical University, Ankara, Turkey  \n2Electrical-Electronics Engineering Department, Ostim Technical University, Ankara, Turkey  \nCorrespondence  \nMurat Simsek, Artificial Intelligence Engineering Department, Ostim Technical University, Ankara, Turkey. [Email:](Email: murat.simsek@ostimteknik.edu.tr)[ murat.simsek@ostimteknik.edu.tr](Email: murat.simsek@ostimteknik.edu.tr)  \n1 | INTRODUCTION  \nAbstract  \nTo ensure the sustainability of customer-company loyalty and to control the financial flow of the company, studies involving customer loss or gain are carried out. When the studies are examined, it is seen that many methods are used for this purpose. Traditional machine learning methods are widely used in loss analysis due to their ability to process large amounts of customer data. In this study, the classification of the customer group in the banking sector has been made. In the context of the banking sector, this study delved into the classification of customer groups, utilizing a dataset and various machine learning models. Since the imbalance in the data set negatively affects the success parameters, the imbalance of the data is also discussed in this study. Within the scope of the study, the results obtained from the models analyzed by applying them to the dataset were evaluated by using F1-score, precision, recall, and model accuracy comparison tools. When the results are compared, it is seen that the proposed random oversampling (ROS)-voting (random forest [RF]-gradient boosting machines [GBM]) model has a better classification and prediction success than the other applied models and an accuracy rate of 95% .  \nKEYWOR DS  \nartificial intelligence, bank, classification, customer churn, machine learning  \nThe concept of customer analytics and its predictive analytical approaches have been used very often to model  \nPeople can go through many changes at different stages of their lives and take part in new structures. These include banking habits. There may be subreasons pushing individuals to this conclusion or it may be encountered in situations where the preference is changed. It is very important to determine the root causes that can be revealed here in terms of determining the roadmap of the banking sector. In cases where preferences are in question, it is predicted that applying quantitative approaches in finding solutions to the problems here will lead to healthier results since social status and psychological factors are involved.  \ncustomer behavior and to make critical business decisions. They are specially used for market/customer segmentation and loss analysis processes. The information obtained from these application areas is used for different purposes such as customer relationship management and direct marketing (Germann et al., 2020; Seymen et al., 2023) .  \nIn customer analytics studies carried out within the scope of CRM, studies on customer loyalty and customer loss include models including data mining, machine learning and deep learning methods, and comparative analyzes of these models. When the studies in the  \nJournal of Forecasting. 2024;43:391–401.  \n[wileyonlinelibrary.com/journal/for](wileyonlinelibrary.com/journal/for)  \n© 2023 John Wiley & Sons Ltd.  \n391  \n392  \nSIMSEK and TAS  \nliterature are examined, it is seen that customer loss studies are mostly used in the telecommunications and finance sectors, which are contract-based sectors that cover the service process of customers with the company. Since they are document-based, customer losses can be determined clearly. It is not easy to make these analyses in some sectors that are not contract-based. The accuracy of the data is often questio","cbCaibtDDnYzEw9M","https://ap.wps.com/l/cbCaibtDDnYzEw9M","pdf",702332,11,"English","# Introduction\n## Customer analytics and churn motivation\n## Machine learning methods for customer loss prediction\n## Ensemble learning and XGBoost","[{\"question\":\"What problem does the study address in the banking sector?\",\"answer\":\"The study addresses customer-group classification for portfolio churn, aiming to support customer loyalty and financial-flow control.\"},{\"question\":\"Which evaluation metrics are used to compare models?\",\"answer\":\"The models are compared using F1-score, precision, recall, and accuracy.\"},{\"question\":\"How does the proposed approach handle imbalanced data?\",\"answer\":\"It applies random oversampling (ROS) and then uses a voting ensemble combining random forest and gradient boosting models.\"}]","A classification application for using learning methods in bank costumer's portfolio churn | PDF",1792185323,28]