From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data

From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data | 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 From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data Yan Li, Guihua Wen, Bo Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4795897/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 At present, deep learning has limited application in the field of financial credit risk because deep learning is good at processing unstructured data such as images, voice, and text, while the credit risk field processes structured tabular data, which makes the existing deep learning methods not well adapted to financial structured data tasks. To this end, this paper proposes a new Table-to-Image Converted Transfer MLP-like network for financial credit risk prediction. First, our method attempts to represent structured data from a new perspective and proposes a data homology based table-to-image conversion method to convert the tabular financial credit risk prediction data into image-like financial data. Then, based on the Strip-MLP structure, a pretrained MLP-like network is proposed to be applied to the credit prediction of the converted image-like financial data. The model is pre-trained with a public financial dataset, and its pre-trained parameters are transferred to the private dataset of financial institutions with different feature numbers and feature contents through transfer learning. Experimental results show that for the task of financial credit risk prediction, the methods proposed in this paper have significantly improved the effect compared with the baseline algorithm. financial credit structured data transfer learning MLP-like model data homology 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-4795897","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341133986,"identity":"b1624809-c182-4139-93fc-2b2e80c2c57c","order_by":0,"name":"Yan Li","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":341133987,"identity":"040b64eb-1ab1-43bd-89e5-8145a1af719f","order_by":1,"name":"Guihua Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYHACNhAhB+UwE6/FGIgZG0jSkthAtBb+2cevPeapuJM+v/2M+QOGCuvEBvazB/BqkTiXU27Mc+ZZ7oYzOYYNDGfSExt48hLwajHg4UmTzm07nLtBgsewgbHtcGKDBI8BEVr+HU6XnwHS8o8oLezHpHMbDicw3ABpaSBCi8QZHjbpP8cOG244k1Y4I+FYunEbTw5+Lfw97M8kZ9QclpdvP7zhw4caa9l+9jP4tTAwIDsjgQEaTfgB+wPCakbBKBgFo2BkAwA5nUI6UVTDYgAAAABJRU5ErkJggg==","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Guihua","middleName":"","lastName":"Wen","suffix":""},{"id":341133988,"identity":"86a8761f-49be-42d6-b076-14f561cf0fe1","order_by":2,"name":"Bo Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-24 13:52:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4795897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4795897/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78415340,"identity":"5e1ae003-bb4a-4512-b44b-ff0532377e55","added_by":"auto","created_at":"2025-03-13 04:31:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":910123,"visible":true,"origin":"","legend":"","description":"","filename":"FromTabletoImageBoostingCreditRiskPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4795897/v1_covered_fe80ec89-424d-438d-bb7d-6506b9868b25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data","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":"financial credit, structured data, transfer learning, MLP-like model, data homology","lastPublishedDoi":"10.21203/rs.3.rs-4795897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4795897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAt present, deep learning has limited application in the field of financial credit risk because deep learning is good at processing unstructured data such as images, voice, and text, while the credit risk field processes structured tabular data, which makes the existing deep learning methods not well adapted to financial structured data tasks. To this end, this paper proposes a new Table-to-Image Converted Transfer MLP-like network for financial credit risk prediction. First, our method attempts to represent structured data from a new perspective and proposes a data homology based table-to-image conversion method to convert the tabular financial credit risk prediction data into image-like financial data. Then, based on the Strip-MLP structure, a pretrained MLP-like network is proposed to be applied to the credit prediction of the converted image-like financial data. The model is pre-trained with a public financial dataset, and its pre-trained parameters are transferred to the private dataset of financial institutions with different feature numbers and feature contents through transfer learning. 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