Transformer-based NLP Approaches for Credit Risk Prediction: A Systematic Review

Transformer-based NLP Approaches for Credit Risk Prediction: A Systematic Review | 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 Transformer-based NLP Approaches for Credit Risk Prediction: A Systematic Review Pfarelo Raliphada, Micheal Olusanya, Seun Olukanmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707021/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 This systematic review explores how Natural Language Processing (NLP) and Large Language Models (LLMs), such as BERT, RoBERTa, and LLaMA, are applied to enhance credit risk classification. Traditional models primarily utilize structured data, but the rising availability of unstructured text has opened new avenues for analysis. We employed a systematic literature review methodology across Scopus, ScienceDirect, and Web of Science, guided by PRISMA principles, and filtered 284 studies down to 63 through semantic similarity scoring. Results reveal transformer-based models substantially improve predictive accuracy, especially when hybridized with temporal and sentiment-aware components. We further discuss ethical implications, including algorithmic bias and regulatory compliance. The review emphasizes the transformative potential of NLP in financial modeling while identifying interpretability and data governance as ongoing challenges. Credit Risk Natural Language Processing (NLP) BERT RoBERTa LLaMA Transformer Models Sentiment Analysis Classification 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. 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-7707021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531584065,"identity":"0d9fdb88-8a2a-44c0-b137-4c334cef92a8","order_by":0,"name":"Pfarelo 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Analysis, Classification","lastPublishedDoi":"10.21203/rs.3.rs-7707021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This systematic review explores how Natural Language Processing (NLP) and Large Language Models (LLMs), such as BERT, RoBERTa, and LLaMA, are applied to enhance credit risk classification. Traditional models primarily utilize structured data, but the rising availability of unstructured text has opened new avenues for analysis. We employed a systematic literature review methodology across Scopus, ScienceDirect, and Web of Science, guided by PRISMA principles, and filtered 284 studies down to 63 through semantic similarity scoring. Results reveal transformer-based models substantially improve predictive accuracy, especially when hybridized with temporal and sentiment-aware components. We further discuss ethical implications, including algorithmic bias and regulatory compliance. The review emphasizes the transformative potential of NLP in financial modeling while identifying interpretability and data governance as ongoing challenges.","manuscriptTitle":"Transformer-based NLP Approaches for Credit Risk Prediction: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 04:00:12","doi":"10.21203/rs.3.rs-7707021/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":"e1a2d644-d67e-4ae3-a14b-6f8adb18fb9e","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T03:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 04:00:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7707021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7707021","identity":"rs-7707021","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}