Leveraging Graph Neural Networks to Detect Anomalies in Mobile Money Transactions

Leveraging Graph Neural Networks to Detect Anomalies in Mobile Money Transactions | 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 Leveraging Graph Neural Networks to Detect Anomalies in Mobile Money Transactions Brian Stephen Ssali, Frank Namugera, Francis Fuller Bbosa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7784995/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 Anomaly detection is a critical task across various domains, including data quality management, healthcare, and finance. Several anomaly detection methods particularly rule-based approaches have been designed mainly working with simple data structures. However, given the evolution in data, traditional rule-based techniques often fall short when dealing with complex datasets with intricate interdependencies such as those on networks. These interdependencies provide extra information that could improve the anomaly detection task. This paper, therefore, introduces an enhanced anomaly detection method leveraging Graph Neural Networks (GNNs), which are adept at capturing patterns in data with rich relational structures. Our approach demonstrates superior performance over conventional rule-based models, achieving a balanced accuracy of 93% and an F1 score of 92.3%, compared to 72% and 58.5% respectively for the traditional methods. These results underscore the efficacy of adopting GNNs in accurately identifying anomalies, with significant implications for applications. Hence the use of GNN instead of rule-based methods in anomaly detection problems would yield more accurate and stable results particularly in problems with complex data. Anomaly detection graph neural network Fraud Mobile Money Full Text Additional Declarations No competing interests reported. Supplementary Files synthetic.csv 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-7784995","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527232255,"identity":"a251ab03-4c36-43d0-8f20-38c5f3f39111","order_by":0,"name":"Brian Stephen Ssali","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"Stephen","lastName":"Ssali","suffix":""},{"id":527232256,"identity":"4a94c258-d3b0-4105-831f-54fa4022c582","order_by":1,"name":"Frank Namugera","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Namugera","suffix":""},{"id":527232257,"identity":"57db129e-d138-4447-9136-1a7380efbc03","order_by":2,"name":"Francis Fuller Bbosa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC9gbmxgMMDAcSgGzGB0CCh4+QFp4DjA0wLcwGIAE2UrSwSYBECGuRSGw48HHPnTx+/sPPKr/m2MmwMTA/fHSDgJaDM549K5ackWZ2W3ZbMtBhbMbGOXi02AO1HOY5cDhxww0Gs9uS25iBWnjYpPFpAdly+A9Iy/nj34olt9UTqYUBpOVAjhnjx22HidDC87DhYM8BkF9yiqUZtx3nYWMm4Bce9uSDD34cAIXY8Y0ff26rtudnb374GJ8WFMDMAyaJVQ4CjD9IUT0KRsEoGAUjBgAAigpQ4Z2ZRKUAAAAASUVORK5CYII=","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"Fuller","lastName":"Bbosa","suffix":""}],"badges":[],"createdAt":"2025-10-05 13:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7784995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7784995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93477851,"identity":"9ad65dc0-17d8-4bf2-a3de-5469299c07df","added_by":"auto","created_at":"2025-10-14 09:34:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479370,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscriptfull.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7784995/v1_covered_6c57a9e7-3741-4bf4-97d9-9599534670c7.pdf"},{"id":93477565,"identity":"2be8ef6b-d893-418b-9860-947702061e1e","added_by":"auto","created_at":"2025-10-14 09:26:37","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27342209,"visible":true,"origin":"","legend":"","description":"","filename":"synthetic.csv","url":"https://assets-eu.researchsquare.com/files/rs-7784995/v1/ef8ea156b5e261e2fb928c47.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLeveraging Graph Neural Networks to Detect Anomalies in Mobile Money Transactions\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":"Anomaly detection, graph neural network, Fraud, Mobile Money","lastPublishedDoi":"10.21203/rs.3.rs-7784995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7784995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnomaly detection is a critical task across various domains, including data quality management, healthcare, and finance. Several anomaly detection methods particularly rule-based approaches have been designed mainly working with simple data structures. However, given the evolution in data, traditional rule-based techniques often fall short when dealing with complex datasets with intricate interdependencies such as those on networks. These interdependencies provide extra information that could improve the anomaly detection task. This paper, therefore, introduces an enhanced anomaly detection method leveraging Graph Neural Networks (GNNs), which are adept at capturing patterns in data with rich relational structures. Our approach demonstrates superior performance over conventional rule-based models, achieving a balanced accuracy of 93% and an F1 score of 92.3%, compared to 72% and 58.5% respectively for the traditional methods. These results underscore the efficacy of adopting GNNs in accurately identifying anomalies, with significant implications for applications. Hence the use of GNN instead of rule-based methods in anomaly detection problems would yield more accurate and stable results particularly in problems with complex data.\u003c/p\u003e","manuscriptTitle":"Leveraging Graph Neural Networks to Detect Anomalies in Mobile Money Transactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 09:26:31","doi":"10.21203/rs.3.rs-7784995/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":"d81a7531-8338-4701-8486-73bd2c879a4d","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T09:26:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 09:26:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7784995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7784995","identity":"rs-7784995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}