Research on Overall Risk Early Warning Model of The X Rural Commercial Bank Based on Deep Learning

Research on Overall Risk Early Warning Model of The X Rural Commercial Bank Based on Deep Learning | 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 Research on Overall Risk Early Warning Model of The X Rural Commercial Bank Based on Deep Learning Wang Shilei, Li Chunling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6774559/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 In order to improve the early warning level of overall risk in rural commercial banks, a set of early warning model for overall risk is constructed from the cap- ital perspective based on deep learning technology. Firstly, principal component analysis (PCA) is utilized to screen the influential early warning indicators; sec- ondly, the risk-weighted assets (RWA) of rural commercial banks are predicted by Gate Recurrent Unit (GRU); and lastly, simulation test is carried out with X rural commercial bank as a case study. The study shows that the proposed model (PCA-GRU) can predict the future risk level of rural commercial banks more accurately and with excellent performance, which provides a new way of think- ing for the overall risk early warning of rural commercial banks, and helps rural commercial banks to identify the risks in advance, reduce the cost of risk man- agement, improve competitiveness, and promote the sustainable development of rural commercial banks. Business and commerce/Business and management Business and commerce/Finance Business and commerce/Information systems and information technology Rural commercial banks Comprehensive risk warning Risk-weighted assets Deep learning 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-6774559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466156768,"identity":"9a971aa4-5c9f-45db-930e-588ed0cf65c3","order_by":0,"name":"Wang Shilei","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Shilei","suffix":""},{"id":466156770,"identity":"178acbc7-cd39-4572-b546-9d46ba346dad","order_by":1,"name":"Li Chunling","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACefaGxAcSFTb1/OwNRGox7Dnw2MDiTFqCZM8BYq254fhMorLtUILBjQQidTDOYE6TuHHmQJ7BzccbbzDU2EQT1MIu3ZZsOaPiTrHk7bRiC4ZjabkNBG2ZcybxtsSZZ4x9t3PMJBgbDhPWwnAj/4P037bDjA03zxCtJSFJQrLtcOKEGzxEagEGcrKBxJk0Y8keoF8SiPELLCrl+NkPb7zxocaGCIchAQOJBFKUQ7SQqmMUjIJRMApGBgAA8HNJOnJK28MAAAAASUVORK5CYII=","orcid":"","institution":"Yanshan University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Chunling","suffix":""}],"badges":[],"createdAt":"2025-05-29 08:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6774559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6774559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89944046,"identity":"cf14f634-f5d1-4ce5-a5c2-1d321ab1f56f","added_by":"auto","created_at":"2025-08-26 16:46:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479986,"visible":true,"origin":"","legend":"","description":"","filename":"BlindManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6774559/v1_covered_479b9599-5fbc-461d-8b62-1639eaae0d41.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Overall Risk Early Warning Model of The X Rural Commercial Bank Based on Deep Learning","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":"Rural commercial banks, Comprehensive risk warning, Risk-weighted assets, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-6774559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6774559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to improve the early warning level of overall risk in rural commercial banks, a set of early warning model for overall risk is constructed from the cap- ital perspective based on deep learning technology. 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