Machine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study

Machine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study | 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 Machine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study Umair Ali, Shah Fahad, Ammar Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4961599/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 The present study explores the utilization of machine learning classifiers for the purpose of forecasting firm bankruptcy. The dataset consisted of financial metrics and was used to evaluate six different classifiers which included; Support Vector Classifier, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Tree, and Random Forest. In terms of accuracy in the original data (96.77%) and scaled data (96.70%), Random Forest Classifier emerged as the best performing classifier. This research indicates that careful choice of a model is crucial and also implies that machine learning has a great potential in improving risk management and financial decision making. The implications of these result for various domains in finance suggest that hybrid models should be researched and explained in better detail by future work to further improve accuracy and transparency. Furthermore, the use of machine learning can raise predictive accuracy among financial institutions, which will lower risks thereby increasing overall performance that contributes to financial stability. Artificial Intelligence and Machine Learning Company Bankruptcy Prediction Machine Learning Classifiers Feature Scaling Classification Accuracy Ensemble Learning Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementalFiles.zip 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4961599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343987964,"identity":"e94f90ff-d3b4-4dd3-a6aa-56c49c514510","order_by":0,"name":"Umair Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYLCCBAMbIMnYeIB4LR8q0kBaGojXwjjjzGEwgzgtBsfPHnzM23bebm37YaAtNTbRhLWcyUs25m27nbztTCJQy7G03AaCWg7kmEmDtJgdAGphbDhMhJbzb8x/87adSzY7/5BYLTdyzIDeP2BndoNYWyRvvEuW+FCRnGB2A2hLAjF+4Tufe/BDgoGdvdn59IcPPtTYENbCwMADJhPBKhMIK0dosSdO8SgYBaNgFIxIAAD81ktf+Q0MpAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-5156-4733","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Umair","middleName":"","lastName":"Ali","suffix":""},{"id":343987965,"identity":"834ec47d-843b-4147-b744-03fa680c15a4","order_by":1,"name":"Shah Fahad","email":"","orcid":"https://orcid.org/0009-0008-9676-430X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shah","middleName":"","lastName":"Fahad","suffix":""},{"id":343987966,"identity":"be5a58c4-00b5-4d0c-854d-3410947b57e7","order_by":2,"name":"Ammar Ali","email":"","orcid":"https://orcid.org/0009-0008-7775-4472","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ammar","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2024-08-23 05:22:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4961599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4961599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63235697,"identity":"17f6d58f-8591-4eb0-9d94-dd4cb1c4b145","added_by":"auto","created_at":"2024-08-26 03:00:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":297852,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearningApproachesforPredictingCompanyBankruptcyAComparativeStudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4961599/v1_covered_4fc16980-3b6b-4755-8ce1-f1b1be5b7acc.pdf"},{"id":63235042,"identity":"29978517-461c-4fa5-a04f-b78259c1d34c","added_by":"auto","created_at":"2024-08-26 02:52:10","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5040621,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-4961599/v1/7af28d2a6b8f7e34291ee31b.zip"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Company Bankruptcy Prediction, Machine Learning Classifiers, Feature Scaling, Classification Accuracy, Ensemble Learning","lastPublishedDoi":"10.21203/rs.3.rs-4961599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4961599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study explores the utilization of machine learning classifiers for the purpose of forecasting firm bankruptcy. The dataset consisted of financial metrics and was used to evaluate six different classifiers which included; Support Vector Classifier, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Tree, and Random Forest. In terms of accuracy in the original data (96.77%) and scaled data (96.70%), Random Forest Classifier emerged as the best performing classifier. This research indicates that careful choice of a model is crucial and also implies that machine learning has a great potential in improving risk management and financial decision making. The implications of these result for various domains in finance suggest that hybrid models should be researched and explained in better detail by future work to further improve accuracy and transparency. Furthermore, the use of machine learning can raise predictive accuracy among financial institutions, which will lower risks thereby increasing overall performance that contributes to financial stability.\u003c/p\u003e","manuscriptTitle":"Machine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 02:52:05","doi":"10.21203/rs.3.rs-4961599/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"6676a770-78e0-459d-99e8-ba87ea0a2ee0","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36455655,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-08-28T03:14:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 02:52:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4961599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4961599","identity":"rs-4961599","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}