Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning

Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated 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 Research Article Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning Milad Rahmati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6026136/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 Detecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fraud patterns. Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. Meanwhile, federated learning enables multiple financial institutions to collaboratively train fraud detection models without directly sharing customer data, thus addressing privacy concerns. To enhance explainability and regulatory compliance, the proposed system integrates Explainable AI (XAI) methods, making fraud detection decisions more transparent. Experimental evaluations on benchmark financial datasets and real-world transactional data reveal that our approach improves fraud detection accuracy by 15–30% while reducing false positives compared to existing machine learning-based solutions. The findings highlight the potential of GNNs and FL in advancing fraud prevention strategies while maintaining data security and interpretability, making it a promising alternative to traditional fraud detection mechanisms. Artificial Intelligence and Machine Learning Financial Fraud Detection Graph Neural Networks Federated Learning Real-Time AI Adaptive Learning Explainable AI Privacy-Preserving Machine Learning Anomaly Detection Scalable AI Cybersecurity in Finance 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-6026136","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415535251,"identity":"d5453ac1-9d4f-4dcc-a234-7eb254379129","order_by":0,"name":"Milad Rahmati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYHACNhBhx0+qFoNkyQZStTBuOECset3Zh589ulHzh9n4Ro7hA4YaO8JazM6lmRvnHDPgM7uRY2zAcCyZCC1nGMykc9gMmM1u5G6TYGxgJkYL+zfpnH8GjJtngLXUE6OFx0w6tw3ofQmwlsNEaSmTzu0zTpY48/6zQcKx40Q5bJt0zjc5O/72tMQHH2qqCWtBBQmkahgFo2AUjIJRgB0AAKQfNFiWSW3FAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0009-8471-0969","institution":"Western University","correspondingAuthor":true,"prefix":"","firstName":"Milad","middleName":"","lastName":"Rahmati","suffix":""}],"badges":[],"createdAt":"2025-02-13 23:04:23","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-6026136/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6026136/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76432400,"identity":"667d2791-e2c5-4def-b8e9-91dee2876158","added_by":"auto","created_at":"2025-02-17 06:58:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505938,"visible":true,"origin":"","legend":"","description":"","filename":"8RealTimeFinancialFraud.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6026136/v1_covered_873f0559-afa0-47b0-ae4b-914be1f95bc3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReal-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning\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":"Financial Fraud Detection, Graph Neural Networks, Federated Learning, Real-Time AI, Adaptive Learning, Explainable AI, Privacy-Preserving Machine Learning, Anomaly Detection, Scalable AI, Cybersecurity in Finance","lastPublishedDoi":"10.21203/rs.3.rs-6026136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6026136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDetecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fraud patterns. Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. Meanwhile, federated learning enables multiple financial institutions to collaboratively train fraud detection models without directly sharing customer data, thus addressing privacy concerns. 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