[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290895-105":59,"doc-detail-290895-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","examining-the-impact-of-income-expense-ratios-on-bank-success-using-machine-learning-methods","Examining The Impact of Income-Expense Ratios on Bank Success Using Machine Learning Methods","","The study examines how income-expense structure ratios relate to bank success in Türkiye using machine learning. Data from 24 banks over 2012–2022 are analyzed, with Net Operating Profit (Loss) divided by Total Assets as the dependent variable. Each year’s average is used to code banks as successful or unsuccessful, enabling evaluation of vulnerabilities and opportunities under varying cyclical conditions. Multiple classifiers are trained on 264 observations, and results highlight XGBoost as highest performing while key ratio drivers are identified.",{"@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/examining-the-impact-of-income-expense-ratios-on-bank-success-using-machine-learning-methods/290895/",{"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/examining-the-impact-of-income-expense-ratios-on-bank-success-using-machine-learning-methods/290895.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How is bank success defined in the study?","Question",{"text":112,"@type":113},"Bank success is coded relative to each year’s average of Net Operating Profit (Loss) / Total Assets: above-average banks are successful, below-average banks are unsuccessful.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning models are used for classification?",{"text":117,"@type":113},"The paper evaluates Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, CART, Random Forest, and Light Gradient Boosting Machine, and reports comparative performance.",{"name":119,"@type":110,"acceptedAnswer":120},"What ratio criteria are found to be most effective for determining bank success?",{"text":121,"@type":113},"Total income / total expenses, net interest income after specific provisions / total assets, and other operating expenses / gross operating profit are identified as the most effective criteria.","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},290895,1789642905,{"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":39,"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":129,"read_time":41,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Examining The Impact of Income-Expense Ratios on Bank Success Using Machine Learning Methods  \nİsmail Dere 1 Münevver Turanlı2 Selçuk Alp 3  \n1,2 İstanbul Ticaret University, Türkiye  \n3 Yıldız Technical University, Statistics Department, Türkiye  \n[1](1 dereismail7@gmail.com)[ dereismail7@gmail.com](1 dereismail7@gmail.com), [2](2 mturanli@ticaret.edu.tr)[ mturanli@ticaret.edu.tr](2 mturanli@ticaret.edu.tr), [3](3 alp@yildiz.edu.tr)[ alp@yildiz.edu.tr](3 alp@yildiz.edu.tr)  \nReceived: Jul 20, 2024 Accepted: Aug 9, 2024 Published: Dec 30, 2024  \nAbstract: The study aims to examine the success of banks in the banking sector in terms of financial ratios. In this context, income-expense structure ratios for 24 selected banks operating in Türkiye for the period 2012-2022 were used. In the study, Net Operating Profit (Loss) / Total Assets was considered as the dependent variable. For each year of the 2012-2022 period, the average of the relevant variable was taken, and banks above the average were coded as successful, while those below were coded as unsuccessful. The examination of the period, especially considering the average taken for each year, allowed for the consideration of any emerging vulnerabilities or opportunities given the different cyclical conditions. Classification algorithms are used to determine which of the predefined classes a data point belongs to. The study predicted the 264 data points for the 24 banks for the period 2012-2022 using machine learning methods (Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, CART, Random Forest, and Light Gradient Boosting Machine) . According to the findings, the highest accuracy, precision, recall, and F1 score values among the machine learning methods used were shown by XGBoost, while the lowest values were shown by the KNN method. When examining the findings in terms of income-expense structure ratios, the most effective criteria for determining the success of a bank's operations were found to be total income / total expenses, net interest income after specific provisions / total assets, and other operating expenses / gross operating profit. The study found that banks maintaining a balance between income and expenses were successful. Among the machine learning methods used to determine the financial success of banks, XGBoost showed the highest performance. Additionally, the most effective criteria for assessing banks' operational success were identified, highlighting that these criteria are significant indicators for understanding bank performance. This study demonstrated the effectiveness of machine learning methods in evaluating the financial structures of banks and provided an important foundation for future research.  \nKeywords: Machine Learning, Banking Sector, Financial Performance  \nJEL Classification: C10 , G20  \n1. Introduction  \nIn the dynamic and ever-evolving financial landscape, the success of banks has become increasingly complex to analyze, with a myriad of factors contributing to their performance. One crucial aspect that has garnered significant attention is the impact of income-expense ratios on bank success, particularly in the context of leveraging machine learning methods to enhance our understanding of these relationships. This research paper aims to delve into the intricate interplay between income-expense ratios and bank success, exploring the potential insights that can be derived through the application of advanced analytical techniques.  \nThe primary objective of this study is to examine the influence of income-expense ratios on the overall success of banks, with a specific focus on the ability of machine learning models to capture and elucidate these relationships. In this context, incomeexpense structure ratios for 24 selected banks operating in Türkiye for the period 2012-2022 were used. In the study, Net Operating Profit (Loss) / Total Assets was considered as the dependent variable. For each year ","cbCaihIi3wSvQdQ9","https://ap.wps.com/l/cbCaihIi3wSvQdQ9","pdf",271461,12,"English","# Introduction\n## Objective and research focus\n## Data and dependent variable definition\n## Machine learning approach and evaluation\n## Practical implications","[{\"question\":\"How is bank success defined in the study?\",\"answer\":\"Bank success is coded relative to each year’s average of Net Operating Profit (Loss) / Total Assets: above-average banks are successful, below-average banks are unsuccessful.\"},{\"question\":\"Which machine learning models are used for classification?\",\"answer\":\"The paper evaluates Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, CART, Random Forest, and Light Gradient Boosting Machine, and reports comparative performance.\"},{\"question\":\"What ratio criteria are found to be most effective for determining bank success?\",\"answer\":\"Total income / total expenses, net interest income after specific provisions / total assets, and other operating expenses / gross operating profit are identified as the most effective criteria.\"}]","Examining The Impact of Income-Expense Ratios on Bank Success Using Machine Learning Methods | PDF"]