An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction

An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction Kejian Tong, Zonglin Han, Yanxin Shen, Yujian Long, Yijing Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8296526/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Credit scoring is vital in the financial industry,assessing the risk of lending to credit card applicants. Traditionalcredit scoring methods face challenges with large datasets anddata imbalance between creditworthy and non-creditworthy applicants.This paper introduces an advanced machine learningand deep learning framework to improve the accuracy andreliability of credit card approval predictions. We utilized extensivedatasets of user application records and credit history,implementing a comprehensive preprocessing strategy, featureengineering, and model integration. Our methodology combinesneural networks with an ensemble of base models, includinglogistic regression, support vector machines, k-nearest neighbors,decision trees, random forests, and gradient boosting. The ensembleapproach addresses data imbalance using Synthetic MinorityOver-sampling Technique (SMOTE) and mitigates overfittingrisks. Experimental results show that our integrated modelsurpasses traditional single-model approaches in precision, recall,F1-score, AUC, and Kappa, providing a robust and scalablesolution for credit card approval predictions. This researchunderscores the potential of advanced machine learning techniquesto transform credit risk assessment and financial decisionmaking. Artificial Intelligence and Machine Learning Credit scoring machine learning deep learning neural networks data imbalance ensemble learning credit card approval financial risk management Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8296526","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556318172,"identity":"fc646f56-00b1-46f2-ace0-c0201dc2371a","order_by":0,"name":"Kejian Tong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PsQqCQBjA8ZMPzuWL1gvCZxAOpCjwVXJxOt8gwhB0yZ6lqTk4copa24WIJkEIB6mMlmxQx4b7Lfdx3J+7I0RR/tcU+wC7amDD1rP4WVxjENHZO8GuieTmCc2vjQa2HstrUYLjS8xv5/kIiS73m8Zb8OiO45A6y6C3nYikehi67rkxYcLimo9OAL0tF7RKGFrtCSmZEwJeuHh0S3hKqMkREFIv7JIcDpYWhzODAbXAWzOkbX/RoxXPivKJ9kmmubgvjL4uk8akQlltoC3H3yD7HRRFUZSaF/26QLBM27Q0AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0002-5127-2711","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Kejian","middleName":"","lastName":"Tong","suffix":""},{"id":556318173,"identity":"2438ee31-a505-40b4-90bb-e0dae1d98a6a","order_by":1,"name":"Zonglin Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zonglin","middleName":"","lastName":"Han","suffix":""},{"id":556318174,"identity":"d3c47699-41b4-451e-9aeb-8074a1805048","order_by":2,"name":"Yanxin Shen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanxin","middleName":"","lastName":"Shen","suffix":""},{"id":556318175,"identity":"cb774ac2-fa3f-43ba-b396-e9382386a489","order_by":3,"name":"Yujian Long","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yujian","middleName":"","lastName":"Long","suffix":""},{"id":556318176,"identity":"ab04cc85-d191-428a-9807-122d3712a7bc","order_by":4,"name":"Yijing Wei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yijing","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-12-06 19:17:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8296526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8296526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97899830,"identity":"29a14284-5382-4d72-a681-c8c914270b96","added_by":"auto","created_at":"2025-12-10 15:44:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244239,"visible":true,"origin":"","legend":"","description":"","filename":"AnIntegratedMachineLearningandDeepLearningFrameworkforCreditCardApprovalPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8296526/v1_covered_77fb6560-4cef-41e8-8b40-75e082011418.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAn Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"N/A","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Credit scoring, machine learning, deep learning, neural networks, data imbalance, ensemble learning, credit card approval, financial risk management","lastPublishedDoi":"10.21203/rs.3.rs-8296526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8296526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCredit scoring is vital in the financial industry,assessing the risk of lending to credit card applicants. Traditionalcredit scoring methods face challenges with large datasets anddata imbalance between creditworthy and non-creditworthy applicants.This paper introduces an advanced machine learningand deep learning framework to improve the accuracy andreliability of credit card approval predictions. We utilized extensivedatasets of user application records and credit history,implementing a comprehensive preprocessing strategy, featureengineering, and model integration. Our methodology combinesneural networks with an ensemble of base models, includinglogistic regression, support vector machines, k-nearest neighbors,decision trees, random forests, and gradient boosting. The ensembleapproach addresses data imbalance using Synthetic MinorityOver-sampling Technique (SMOTE) and mitigates overfittingrisks. 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