Credit Scoring Enhancement via Ensemble Learning and Self-Organizing Map-Based Feature Transformation

Credit Scoring Enhancement via Ensemble Learning and Self-Organizing Map-Based Feature Transformation | 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 Article Credit Scoring Enhancement via Ensemble Learning and Self-Organizing Map-Based Feature Transformation Helmi Ayari, Ramzi Guetrai, Naoufel Naoufel Kraiem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7358520/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 a fundamental component of financial risk assessment, yet conventional modeling techniques often exhibit limited effectiveness in addressing critical challenges such as class imbalance and complex, non-linear feature interactions. To overcome these constraints, this study introduces a novel ensemble-based credit scoring framework designed to enhance both predictive performance and model robustness across heterogeneous financial datasets. Central to the proposed methodology is the application of self-organizing maps (SOMs) for unsupervised feature transformation. This technique systematically restructures the input space to preserve topological and clustering properties, thereby capturing latent structural relationships within the data. SOM hyperparameters are meticulously tuned using grid search to ensure optimal representational fidelity. The transformed feature set is subsequently integrated into a stacked ensemble architecture, wherein diverse base learners are combined via a meta-learner to exploit model complementarity. To mitigate the effects of class imbalance and optimize decision boundaries, an F1-score-driven threshold calibration strategy is employed. The framework is empirically validated on five benchmark credit scoring datasets, achieving classification accuracies of 84.00% (German), 92.03% (Australian), 87.68% (Japanese), 99.00% (Polish~1), and 96.77% (Taiwan), consistently outperforming baseline models. These results underscore the robustness and efficacy of the proposed approach in complex credit risk environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. 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. 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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-7358520","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539011468,"identity":"8c1dc3ba-950b-42e3-98ba-e867e59b1912","order_by":0,"name":"Helmi Ayari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAwbmBoYEMJOx8QGRWhjhWpoNQHwwOEBICxSwSRClxZy9sXXDAwa7xP7Zh9sqPu74w2A+u8eA+eMO3Fosew623UhgSE6ccS6x7ebMMwYMMneAxMEzeBx2IxGkhTmx4Qxj223eNgMGCYkcoJY2PFruPwRpqU+cD9RS/JcoLTcYQVoOJ24AamFmJErLGZDDDI4bbzzD2CzZe8aYR0IireDAWXxajh8+dvNHRbXsvDPsDz/83CEnJyGRvPFBJR4tUI0Mjg0gGhhHPCD6ACENIGDPANUyCkbBKBgFowADAAAw/FevNY3ImwAAAABJRU5ErkJggg==","orcid":"","institution":"Tunisia Polytechnic School","correspondingAuthor":true,"prefix":"","firstName":"Helmi","middleName":"","lastName":"Ayari","suffix":""},{"id":539011469,"identity":"b40eed01-d734-4104-b963-0528653673bd","order_by":1,"name":"Ramzi Guetrai","email":"","orcid":"","institution":"Novobit Group GmbH","correspondingAuthor":false,"prefix":"","firstName":"Ramzi","middleName":"","lastName":"Guetrai","suffix":""},{"id":539011470,"identity":"e9b3fa33-05a5-43ac-a5aa-e4b11ac45865","order_by":2,"name":"Naoufel Naoufel Kraiem","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Naoufel","middleName":"Naoufel","lastName":"Kraiem","suffix":""}],"badges":[],"createdAt":"2025-08-12 18:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7358520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7358520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95180171,"identity":"21d7c1d9-9338-408c-bb5d-aff03df41674","added_by":"auto","created_at":"2025-11-05 08:17:01","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5975,"visible":true,"origin":"","legend":"","description":"","filename":"feee56cc681747008bbf0784fc58f69d.json","url":"https://assets-eu.researchsquare.com/files/rs-7358520/v1/25642a1b4dc27b2d343bb6eb.json"},{"id":98627398,"identity":"3dc9f379-bf8f-4b51-af24-3b396b30fd93","added_by":"auto","created_at":"2025-12-19 17:10:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510192,"visible":true,"origin":"","legend":"","description":"","filename":"sbvmbbxzxwjyvqjvswqtmmdmxnjsztnj.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7358520/v1_covered_79f3acda-1164-44c0-a1f8-9f3847f7ebc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Credit Scoring Enhancement via Ensemble Learning and Self-Organizing Map-Based Feature Transformation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7358520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7358520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Credit scoring is a fundamental component of financial risk assessment, yet conventional modeling techniques often exhibit limited effectiveness in addressing critical challenges such as class imbalance and complex, non-linear feature interactions. 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