[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117935-en":3,"doc-seo-117935-105":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},117935,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Reinforcement of the Bank Loan Model - Feature Selection and Machine Learning","The study examines whether feature selection combined with machine learning can guarantee effective bank credit risk model performance while addressing the practical need for auditability, transparency, and explainability. It argues that current credit scoring often relies on simple classifiers, limiting the predictive potential of modern ML. A framework compares models before and after feature selection on bank loan test data, using interpretability-oriented evaluation against scorecards. Results indicate ML can improve predictive power while preserving comparable interpretability.","Reinforcement of the Bank Loan Model using the Feature Selection Method of Machine Learning  \nNoopur Goel1, Durgesh Kumar Singh2  \n1Head, Department of Computer Applications  \nVBS Purvanchal University  \nJaunpur, India  \n[noopurt11@gmail.com](noopurt11@gmail.com)  \n2Research Scholar, Department of Computer Applications  \nVBS Purvanchal University  \nJaunpur, India  \n[durgeshsingh111@gmail.com](durgeshsingh111@gmail.com)  \nAbstract— Does feature selection and machine learning (ML) guarantee the effectiveness of the bank credit system model? This article aims to analyze this problem. In fact, in finance, expert-based credit risk models still dominate. In this study, we establish a new benchmark using consumer data and present machine learning methods. A risk prediction that is as accurate as possible is an important requirements for credit scoring models. In addition, regulators expect that the models should to be auditable and transparent. As a result, the superior predictive power of contemporary machine learning algorithms cannot be fully utilized in credit scoring because very simple predictive models, such as several ML classifiers, are still widely used. As a result, significant potential is missed, increasing reserves or the number of credit defaults. A framework for comparing scores before and after feature selection machine learning models that are transparent, auditable, and explainable is presented in this article, as well as the various dimensions that need to be taken into consideration in order to make credit scoring models understandable. In accordance with this framework, we give an overview of the models which demonstrate how it can be used in credit scoring, and compare the results to scorecards' interpretability. The model presented demonstrates that machine learning techniques can maintain their ability to enhance predictive power while still maintaining a comparable level of interpretability.  \nKeywords-Bank credit, Machine learning, Feature selection, Ensemble, Voting, Stacking, ROC (AUC) curve.  \nI. INTRODUCTION  \nCredit scoring systems aim to satisfy a minimum-loss principle for the sustainability of lending institutions by providing clients with a probability of default [1] . As a result, a credit scoring system aids in the decision-making process for credit applications, manages credit risks, and hasan impact on the number of non-performing loans that are likely to result in bankruptcy, a financial crisis, or environmental sustainability. Although credit officers or expert-based credit scoring models have been determining whether borrowers can meet their requirements over the past ten years, this has changed over time due to technological advancements. In order to lessen each lending institution's potential loss, this modification necessitates the establishment of an automated credit decision-making system that can avoid opportunity losses or credit losses [2] . Because of this, the increasing number of financial services that do not involve a human being has made it increasingly important in recent years to use automated credit scoring. To put it another way, an accurate credit scoring model is needed for modern lending institutions to use technology and automation to cut down on operating costs. Although developing an effective model for determining a client's  \ncreditworthiness is extremely challenging, machine learning is now an essential component of credit scoring applications [3] . It is stated that the utilization of intricate algorithms in the context of the application of machine learning to financial services might lead to a lack of transparency for customers. Provide consumers, auditors, and supervisors with an explanation of a credit score and the resulting credit decision when challenged when using machine learning to assign credit scores and make credit decisions is typically more challenging. As a result, model developers face an increasing demand for tools to comprehend what their models","cbCaiuAxtm1ha2sk","https://ap.wps.com/l/cbCaiuAxtm1ha2sk","pdf",729780,1,12,"English","en",105,"# Introduction\n## Credit scoring objectives and challenges\n## Machine learning and transparency requirements\n## Feature selection approaches\n# Methodology\n## Base classifiers and ensemble strategies (voting, stacking)\n## Feature selection methods and selected features\n## Model comparison design\n# Evaluation\n## Performance metrics and interpretability comparison\n## Results using test dataset\n# Conclusion","[{\"question\":\"Why does the paper focus on feature selection in bank loan credit scoring?\",\"answer\":\"The paper targets whether feature selection plus machine learning can enhance effectiveness, while also improving auditability and transparency that regulators and stakeholders expect.\"},{\"question\":\"How does the study compare models before and after feature selection?\",\"answer\":\"It evaluates base machine learning classifiers and ensemble methods, then extracts important features using multiple selection techniques and measures performance on bank loan test data using comparative metrics.\"},{\"question\":\"Which performance and evaluation tools are used in the model comparison?\",\"answer\":\"The paper references metrics such as precision, recall, F1-score, confusion matrix, and ROC (AUC) curve, and also compares interpretability against scorecards.\"}]","Reinforcement of the Bank Loan Model - Feature Selection and Machine Learning | PDF",1785680439,30,{"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},"reinforcement-of-the-bank-loan-model-feature-selection-and-machine-learning","",{"@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/reinforcement-of-the-bank-loan-model-feature-selection-and-machine-learning/117935/",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","2026-08-02",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},"Why does the paper focus on feature selection in bank loan credit scoring?","Question",{"text":75,"@type":76},"The paper targets whether feature selection plus machine learning can enhance effectiveness, while also improving auditability and transparency that regulators and stakeholders expect.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study compare models before and after feature selection?",{"text":80,"@type":76},"It evaluates base machine learning classifiers and ensemble methods, then extracts important features using multiple selection techniques and measures performance on bank loan test data using comparative metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which performance and evaluation tools are used in the model comparison?",{"text":84,"@type":76},"The paper references metrics such as precision, recall, F1-score, confusion matrix, and ROC (AUC) curve, and also compares interpretability against scorecards.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]