[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121202-en":3,"doc-seo-121202-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121202,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models - Research Study","Banking growth and intensifying competition increase the need to retain existing customers to reduce potential losses. This study builds churn forecasts using a 1,750,036-customer demographic dataset labeled for churned versus retained clients. Five machine learning models are trained and compared for binary classification performance: Decision Tree, Random Forest, Gradient Boost, CatBoost, and LGBM. Results show LGBM achieves the strongest recall and accuracy, with the fewest false negatives, supported by accuracy 0.8789, precision 0.8978, recall 0.8553, F1 0.8758, and AUC 0.9694.","Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models  \nL W Widianti1, A S B Karno2, W Hastomo*3, A N Utomo4, D Arif5, I S K Wardhana6, D Strydom7  \n1Department of Information System; STMIK Jakarta STI&K, Jakarta, Indonesia  \n2,5Department of Information System, Faculty of Engineering, Gunadarma University, Depok, Indonesia  \n3Department of Information Technology, Ahmad Dahlan Institute of Technology and Business, Indonesia  \n4Department of Information Technology, Institut Sains dan Teknologi Nasional, Jakarta, Indonesia  \n6Department of Information System Faculty of Engineering and Computer Science, Indraprasta University PGRI  \n7Weskaap Motors corporate, 87 Main Street, Western Cape, Vredenburg 7380, South Africa  \nE-mail: [lindawewe100@gmail.com](lindawewe100@gmail.com1)[1](lindawewe100@gmail.com1), [adh1t10.2@gmail.com](adh1t10.2@gmail.com2)[2](adh1t10.2@gmail.com2), [Widie.has@gmail.com](Widie.has@gmail.com3)[3](Widie.has@gmail.com3), [aryo.nurutomo@gmail.com](aryo.nurutomo@gmail.com4)[4](aryo.nurutomo@gmail.com4), [dodiarif8@gmail.com](dodiarif8@gmail.com5)[5](dodiarif8@gmail.com5), [indraskw@gmail.com](indraskw@gmail.com6)[6](indraskw@gmail.com6),  \n[deon@wkm.co.za](deon@wkm.co.za7)[7](deon@wkm.co.za7)  \nAbstract. The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM) . According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.  \nKeywords: Customer Loyalty Forecasting; Churn Prediction; Machine Learning; Financial Customer Analytics  \n1. Introduction  \nCustomer retention is crucial for the banking industry's profitability and growth, as retaining clients directly impacts long-term revenue stability and reduces operational costs [1] . However, banks confront difficulties in maintaining clients due to fierce competition and rising customer turnover rates [2] . Recent research indicates that more than 20% of bank clients switch institutions annually, driven by factors such as dissatisfaction with service quality and competitive offers [3] . With the cost of acquiring new customers estimated to be five times higher than retaining existing ones [4], banks have prioritized client retention asa strategic focus. Advanced machine learning analytics and models enable banks to accurately estimate client churn risk and design personalized retention strategies.  \nTo further improve model performance, techniques such as tree pruning (to reduce overfitting), ensemble learning (to combine weak classifiers), and hyperparameter tuning [5] (to optimize algorithmic parameters) have proven effective in enhancing predictive accuracy [6]. Recent studies by [7] highlight that optimized machine learning models, such as gradient-boosted trees and deep neural networks, outperform classical logistic reg","cbCairwBlDLaJMZ6","https://ap.wps.com/l/cbCairwBlDLaJMZ6","pdf",1126921,1,16,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n# 2. Method\n## Preparing Dataset Loading Dataset\n## Creating and Evaluating Models\n# 3. Results and Discussion","[{\"question\":\"What dataset is used to train the churn prediction models?\",\"answer\":\"The study uses a 1,750,036-customer demographic dataset with records for customers who left and those who remained.\"},{\"question\":\"Which machine learning models are compared in the research?\",\"answer\":\"Five models are evaluated: Decision Tree, Random Forest, Gradient Boost, CatBoost, and Light Gradient Boosting Machine (LGBM).\"},{\"question\":\"Why does the study conclude that LGBM is the best performer?\",\"answer\":\"LGBM is reported to deliver the highest recall and accuracy with the fewest false negatives, along with strong metrics including AUC 0.9694.\"}]","Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models - Research Study | PDF",1785734331,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improved-banking-customer-retention-prediction-based-on-advanced-machine-learning-models-research-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improved-banking-customer-retention-prediction-based-on-advanced-machine-learning-models-research-study/121202/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset is used to train the churn prediction models?","Question",{"text":75,"@type":76},"The study uses a 1,750,036-customer demographic dataset with records for customers who left and those who remained.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the research?",{"text":80,"@type":76},"Five models are evaluated: Decision Tree, Random Forest, Gradient Boost, CatBoost, and Light Gradient Boosting Machine (LGBM).",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the study conclude that LGBM is the best performer?",{"text":84,"@type":76},"LGBM is reported to deliver the highest recall and accuracy with the fewest false negatives, along with strong metrics including AUC 0.9694.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]