[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290914-105":59,"doc-detail-290914-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","forecasting-member-churn-in-medical-insurance-through-machine-learning-analysis","Forecasting Member Churn in Medical Insurance through Machine Learning Analysis","","The insurance industry faces escalating customer churn as digital technologies make policy comparison, product discovery, and provider switching easier. This intensifies competition and drives revenue losses because acquiring new customers is typically more costly than retaining existing ones. The research proposes a medical insurance churn prediction system that uses machine learning to forecast whether customers are likely to churn, supporting proactive retention strategies. Logistic Regression, Random Forest, SVM, and ANN are evaluated, with Random Forest reaching 90% accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@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/forecasting-member-churn-in-medical-insurance-through-machine-learning-analysis/290914/",{"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/forecasting-member-churn-in-medical-insurance-through-machine-learning-analysis/290914.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is member churn a critical issue in the medical insurance industry?","Question",{"text":113,"@type":114},"Member churn increases revenue loss because acquiring new customers costs more than retaining existing ones, and churn disrupts the provider–policyholder relationship.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is the main goal of the proposed research system?",{"text":118,"@type":114},"To predict whether an existing medical insurance customer is likely to churn, enabling proactive retention strategies.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning model performed best and what accuracy was achieved?",{"text":122,"@type":114},"The Random Forest Classifier performed best, achieving 90% accuracy.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290914,1789642944,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"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":130,"read_time":144,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","INTI JOURNAL | Vol.2023:65  \neISSN:2600-7320  \nForecasting Member Churn in Medical Insurance through Machine Learning Analysis  \nChee Wen Jet 1, Goh Ching Pang 1*  \n1 Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia  \n*Email: [gohcp@tarc.edu.my](gohcp@tarc.edu.my)  \nAbstract  \nThe insurance industry faces an escalating challenge with increasing customer churn, spurred by global advancements in technology. The ease with which customers can compare policies, explore new offers, and switch providers online has intensified industry competition. This phenomenon has led to substantial revenue loss for many companies, as acquiring new customers often incurs higher costs than retaining existing ones. Recognizing the paramount importance of client retention, this research addresses the issue by proposing a Churn Prediction System tailored for the medical insurance sector. The system leverages machine learning models to forecast whether an existing customer is likely to churn, crucial for proactive retention strategies. To determine the most effective algorithm for this task, four models—Logistic Regression, Random Forest Decision Tree, Support Vector Machine, and Artificial Neural Network—are tested. The Random Forest Classifier emerges as the optimal performer which achieves accuracy of 90% .  \nKeywords  \nLogistic Regression, Random Forest Decision Tree, Support Vector Machine, Artificial Neural  \nNetwork, Churn Analysis  \nIntroduction  \nHealth constitutes the cornerstone of a fulfilling life, yet Malaysians face challenges in maintaining optimal well-being. Alarming statistics from Malaysia's health minister reveal that one in two Malaysians grapples with obesity and overweight issues, contributing to persistently high rates of heart disease and obesity (BERNAMA, 2021). The silver lining emerges as an increasing number of Malaysians recognize the significance of medical insurance, spurred by the profound impact of the Covid-19 pandemic on health (Raynaud et al., 2021) . Notably, a study by Balqis-Ali (2021) indicates a rise in medical insurance adoption, with 43.4% of Malaysians now covered—a noteworthy increase from previous years.  \nMedical insurance, elucidated by Abdul Rahman (2010), serves as a financial safeguard, covering medical and surgical costs for policyholders who pay monthly premiums to insurance providers. This protective measure ensures financial resilience during emergencies, alleviating concerns about escalating medical expenses. Simultaneously, insurance companies leverage  \nSubmission: 14 November 2023; Acceptance: 28 November 2023  \nCopyright: © 2023. All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nINTI JOURNAL | Vol.2023:65  \neISSN:2600-7320  \npremiums to fuel investments, as outlined by Sheth and Pawar (2021), forming a symbiotic relationship with policyholders. However, the harmony in this mutualistic relationship faces disruption due to customer churn—an issue outlined by Begum et al (2023), where customers cease using a company's products or services. Customer churn is a critical concern, given that acquiring new customers costs significantly more than retaining existing ones, as demonstrated by Claudine and Tobin (2023) . Moreover, a mere 5% increase in customer retention can increase acompany's profits by at least 25% . To address the challenge of customer churn, understanding its underlying causes is paramount. Various factors, such as dissatisfaction with pricing, mismatched product offerings, or competition, contribute to churn (Sid","cbCaihiB3eJFBUlE","https://ap.wps.com/l/cbCaihiB3eJFBUlE","pdf",160302,"English","# Abstract\n# Keywords\n# Introduction\n# Methodology","[{\"question\":\"Why is member churn a critical issue in the medical insurance industry?\",\"answer\":\"Member churn increases revenue loss because acquiring new customers costs more than retaining existing ones, and churn disrupts the provider–policyholder relationship.\"},{\"question\":\"What is the main goal of the proposed research system?\",\"answer\":\"To predict whether an existing medical insurance customer is likely to churn, enabling proactive retention strategies.\"},{\"question\":\"Which machine learning model performed best and what accuracy was achieved?\",\"answer\":\"The Random Forest Classifier performed best, achieving 90% accuracy.\"}]","Forecasting Member Churn in Medical Insurance through Machine Learning Analysis | PDF",13]