Research on a Green Money Laundering Identification Framework and Risk Monitoring Mechanism Integrating Artificial Intelligence and Environmental Governance Data

Research on a Green Money Laundering Identification Framework and Risk Monitoring Mechanism Integrating Artificial Intelligence and Environmental Governance 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 Research on a Green Money Laundering Identification Framework and Risk Monitoring Mechanism Integrating Artificial Intelligence and Environmental Governance Data Xiaoxiong Gu, Jingwen Yang, Min Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7927453/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 Focusing on environmental crime proceeds and the "carbon credit/green bond" channel, we construct a weakly supervised AML framework integrating ESG multimodal signals (carbon registries, emissions disclosures, supply chain incidents, negative news) × transaction networks. Using 1,800 cross-border counterparties, 5 years of data, and 14 carbon-related typology rules as label functions, the model achieves ROC-AUC=0.972 and PR-AUC+28% on greenwashing-related capital chains. It provides +2.4 days of advance warning for high-emission anomalies and suspicious carbon credit rotations; False positive rate reduced by 21%, with significantly elevated risk quantiles (p<0.01) for affected account groups within ESG event windows. This approach translates ESG transparency into actionable AML monitoring capabilities, balancing sustainable finance with national ecological enforcement requirements. Theoretical Computer Science Artificial Intelligence and Machine Learning ESG carbon credits greenwashing weakly supervised learning multimodal fusion early warning cross-border transactions Full Text Additional Declarations The authors declare no competing interests. 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-7927453","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533672168,"identity":"8145d030-f7e7-41e5-84e9-2dd228bef180","order_by":0,"name":"Xiaoxiong Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYNACAwYGNvbmgw+ATB4+orXw8RxLNgBpYSPaIjmJHDMJEIOgFvmI5GMSHwoOJ7ZJpKVVfs2xk2FjYH746AYeLYY30tIkZxikJbbxPD52W3ZbMtBhbMbGOfi0zMgxu81jYJPYxp6WdltyGzNQCw+bNEEtfwwkEtsYcsyKJbfVE9YiD/T1bQaQLRw5Zowftx0mrMWA51n6zx6DNOM2YCBLM247zsPGTMAv8u3Jhw1+/DksO7+9+eDHn9uq7fnZmx8+xmvLASQOMw+YxKMcbEsDEofxBwHVo2AUjIJRMDIBAHW4RX1/zL2jAAAAAElFTkSuQmCC","orcid":"","institution":"UBS Business Solutions (China) Limited, Shanghai, China, 200120","correspondingAuthor":true,"prefix":"","firstName":"Xiaoxiong","middleName":"","lastName":"Gu","suffix":""},{"id":533672169,"identity":"5e10997f-c617-4f5a-8b8b-1644d4e5cef9","order_by":1,"name":"Jingwen Yang","email":"","orcid":"","institution":"University College London, London, United Kingdom","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Yang","suffix":""},{"id":533672170,"identity":"2b331ce2-e8bc-46ce-a589-2a8d3498fc75","order_by":2,"name":"Min Liu","email":"","orcid":"","institution":"HSBC Bank (China) Company Limited, Shenzhen, China, 518000","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-10-23 02:21:29","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-7927453/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7927453/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94243111,"identity":"c06f5860-a3bc-4981-9518-94e6076c3819","added_by":"auto","created_at":"2025-10-24 04:37:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":802464,"visible":true,"origin":"","legend":"","description":"","filename":"10.20EDAI2025XiaoxiongGu3zz3505.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7927453/v1_covered_427de69f-10a9-41e4-9cbd-22cf2f5ac381.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResearch on a Green Money Laundering Identification Framework and Risk Monitoring Mechanism Integrating Artificial Intelligence and Environmental Governance Data\u003c/strong\u003e\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":"ESG, carbon credits, greenwashing, weakly supervised learning, multimodal fusion, early warning, cross-border transactions","lastPublishedDoi":"10.21203/rs.3.rs-7927453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7927453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFocusing on environmental crime proceeds and the \"carbon credit/green bond\" channel, we construct a weakly supervised AML framework integrating ESG multimodal signals (carbon registries, emissions disclosures, supply chain incidents, negative news) × transaction networks. 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