Using Gradient Boosting Machines (GBM) algorithm to enhance mobile money system

Using Gradient Boosting Machines (GBM) algorithm to enhance mobile money system | 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 Using Gradient Boosting Machines (GBM) algorithm to enhance mobile money system Joshua Mortey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8215975/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 This study proposes a machine learning framework to enhance mobile money security through fraud detection. Motivated by increasing threats like fraud and unauthorized access, it evaluates Gradient Boosting Machine (GBM) algorithms XGBoost, LightGBM, CatBoost, and AdaBoost using a public mobile transaction dataset. After preprocessing, training, and testing, model performance is assessed using accuracy, precision, recall, F1-score, and AUC. Results show GBM models outperform traditional methods, with XGBoost and CatBoost achieving about 99% detection accuracy and high precision. These models demonstrate strong potential for real-time fraud detection, enhancing financial security, inclusion, and user trust. The study also suggests further optimization for resource-limited environments and evolving cyber threats. Systems Engineering Mobile Money System Security Machine Learning 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-8215975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551387286,"identity":"3136f07a-4f34-4530-bca8-e6d6db836d93","order_by":0,"name":"Joshua Mortey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYLACxgYbBgYJErWkka7lMAlaDI63P5P4ueN8Yv/s5oMPGGpsoglrOXPGTLL3zO3EGXeOJRswHEvLbSCo5UYOmwRv2+3Ehhs5ZhJAFxKh5f7zZ5J/284lzideyw0GM2netgOJG4jWInkmx9hati3ZeOONtGSDBGL8wnf8+MObb9vsZOfdSD744EONDWEtCgcgtCNYZQIh5SAgDzXUnhjFo2AUjIJRMEIBABhcRbOmrotAAAAAAElFTkSuQmCC","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Mortey","suffix":""}],"badges":[],"createdAt":"2025-11-26 20:15:12","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8215975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8215975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97249493,"identity":"4e721206-74ea-4d00-a445-55ed0db5924d","added_by":"auto","created_at":"2025-12-02 13:12:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":428971,"visible":true,"origin":"","legend":"","description":"","filename":"JoshuaBasedPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8215975/v1_covered_43df498a-5210-4875-ba0e-c68c8f190030.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUsing Gradient Boosting Machines (GBM) algorithm to enhance mobile money system\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kwame Nkrumah University of Science and Technology","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":"Mobile Money, System Security, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8215975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8215975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a machine learning framework to enhance mobile money security through fraud detection. 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