[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118130-en":3,"doc-seo-118130-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118130,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Forecasting Creditworthiness in Credit Scoring Using Machine Learning Methods","An analytical overview links modern machine learning algorithms to credit scoring, focusing on light gradient boosting machine (LGBM), logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT), gradient boosting, and extreme gradient boosting (XGB). The content evaluates how each model’s characteristics affect the accuracy and reliability of borrower creditworthiness predictions and outlines key advantages and limitations. It also reviews current trends, highlights obstacles in model selection, interpretability, and ongoing methodology updates required as financial markets evolve.","Forecasting creditworthiness in credit scoring using machine  \nlearning methods  \nAyagoz Mukhanova1, Madiyar Baitemirov1, Azamat Amirov2, Bolat Tassuov3, Valentina Makhatova4, Assemgul Kaipova5, Ulzhan Makhazhanova1, Tleugaisha Ospanova1  \n1Department of Information Systems, L. N. Gumilyov Eurasian National University, Astana, Republic of Kazakhstan 2Digitalization Department, Abylkas Saginov Karaganda Technical University, Karaganda, Republic of Kazakhstan 3Faculty of Natural Sciences, Non-profit Limited Liability Company, M.H. Dulaty Taraz State University, Taraz, Republic of Kazakhstan 4Department of Software Engineering, Atyrau State University Kh. Dosmukhamedova, Atyrau, Republic of Kazakhstan 5Department of Biostatistics, Bioinformatics and Information Technologies, Astana Medical University, Astana, Republic of Kazakhstan  \nArticle history:  \nReceived Feb 27, 2024 Revised Jun 20, 2024 Accepted Jul 1, 2024  \nKeywords:  \nCreditworthiness  \nDecision tree classifier Gradient boosting classifier Linear discriminant analysis Logistic regression Machine learning  \nCorresponding Author:  \nThis article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.  \nThis is an open access article under the CC BY-SA license.  \nMadiyar Baitemirov  \nDepartment of Information Systems, L. N. Gumilyov Eurasian National University 010000 Astana, Republic of Kazakhstan  \n[Email: madiyar.baytemirov@inbox.ru](Email: madiyar.baytemirov@inbox.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the modern world of finance, where competition in the lending market [1]–[3] is constantly growing, the relevance of developing effective methods for predicting creditworthiness is high. The accuracy and reliability of such methods are key factors for financial institutions seeking to minimize risk and ensure the sustainability of their loan portfolios. Light gradient boosting machine (LGBM) classifier [4]–[6], logistic regression [7]–[9], linear discriminant analysis [10]–[13], decision tree classifier [13], [14], gradient boosting classifier [15]–[17] and extreme gradient boosting (XGB) classifier [18], [19] are a variety of machine training, each with its own unique characteristics and applications. Their use in credit scoring [20]–[22] opens up new opportunities for improving the accuracy of forecasts, especially when working with large volumes of data and complex credit models. In this study, we will also address issues of model  \ninterpretability, computational complexity, and potential limitations. These aspects play an important role in the implementation of research results in real financial practices. Research into creditworthiness using machine","cbCaioQREQtHsHC9","https://ap.wps.com/l/cbCaioQREQtHsHC9","pdf",613444,1,9,"English","en",105,"# Introduction\n# Machine Learning Methods for Credit Scoring\n## Model Interpretability and Complexity\n## Comparative Analysis and Evaluation\n# Trends, Challenges, and Prospects","[{\"question\":\"Which machine learning models are covered for credit scoring creditworthiness forecasting?\",\"answer\":\"The document discusses LGBM, logistic regression, linear discriminant analysis, decision tree, gradient boosting, and extreme gradient boosting (XGB) classifiers.\"},{\"question\":\"How does the paper assess the suitability of each model?\",\"answer\":\"It evaluates each method’s applicability and effectiveness by analyzing its advantages and limitations and their impact on accuracy and reliability in predicting credit risk.\"},{\"question\":\"What implementation considerations are emphasized beyond predictive performance?\",\"answer\":\"The paper highlights the importance of model interpretability, computational complexity, careful model selection, and continuously updating methodology due to changing financial market conditions.\"}]","Forecasting Creditworthiness in Credit Scoring Using Machine Learning Methods | 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