Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh

Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh | 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 Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh Sharun Akter Khushbu, Fariha Tasmin Jaigirdar, Adnan Anwar, Ohidujjaman Tuhin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4659691/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 The growth of mobile financial services over the last decade is well accepted by the people of Bangladesh. However, although reported here and there, a complete understanding of illegal payments and fraudulent transactions is still missing. This paper studies one of the popular and experienced financial transaction services of Bangladesh, Bkash, to identify fraudulent text messages through the service. We propose an improved classification method for fraudulent messages through feature extraction and a hybrid deep learning (DL) approach with ML, which combines a convolutional neural network with sequential learning, referred to as the customized Bkash fraud semantic sequential learning (CBF-SSL) model. Seven machine-learning classifiers and five deep-learning sequential models are experimentally evaluated within the hybrid classifier. The performance of the fraud detection system is assessed using the loss function and confusion matrix on a dataset of 500 Bengali text messages. The performance evaluation demonstrates that the hybrid model of convolution neural network (CNN) using long short-term memory (LSTM) outperforms all other classifiers with the lowest error 1 rate of 0.2532%, the highest F1-measure of 75.00% with the highest test accuracy. The proposed text features in combination with CNN and LSTM prove to be highly effective in detecting fraudulent messages. Bengali text Bkash services convolution neural network deep learning fraudulent transactions semantic sequential learning 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. 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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-4659691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325918010,"identity":"12911de1-2dda-4882-bd4f-a44999b99c52","order_by":0,"name":"Sharun Akter Khushbu","email":"","orcid":"","institution":"Daffodil International University","correspondingAuthor":false,"prefix":"","firstName":"Sharun","middleName":"Akter","lastName":"Khushbu","suffix":""},{"id":325918011,"identity":"50fb5b2a-ed4c-46d7-b74c-88013175b705","order_by":1,"name":"Fariha Tasmin Jaigirdar","email":"data:image/png;base64,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","orcid":"","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Fariha","middleName":"Tasmin","lastName":"Jaigirdar","suffix":""},{"id":325918012,"identity":"03eb41fe-9996-4a9b-bfbe-448a601d5eea","order_by":2,"name":"Adnan Anwar","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Adnan","middleName":"","lastName":"Anwar","suffix":""},{"id":325918013,"identity":"9a6af82b-6799-45e0-9ab9-352f83dcfcba","order_by":3,"name":"Ohidujjaman Tuhin","email":"","orcid":"","institution":"Daffodil International University","correspondingAuthor":false,"prefix":"","firstName":"Ohidujjaman","middleName":"","lastName":"Tuhin","suffix":""}],"badges":[],"createdAt":"2024-06-29 14:19:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4659691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4659691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76130278,"identity":"a0b6a5c0-395f-4ca1-b505-3be97f57381a","added_by":"auto","created_at":"2025-02-12 15:09:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1504937,"visible":true,"origin":"","legend":"","description":"","filename":"FraudAttemptsIdentificationBasedonSemanticSequentialLearningforFinancialTransactionsthroughMobileServicesinBangladesh.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659691/v1_covered_7e81c750-e887-4389-aa3d-026b4cb8697c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh","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":"Bengali text, Bkash services, convolution neural network, deep learning, fraudulent transactions, semantic sequential learning","lastPublishedDoi":"10.21203/rs.3.rs-4659691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4659691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The growth of mobile financial services over the last decade is well accepted by the people of Bangladesh. However, although reported here and there, a complete understanding of illegal payments and fraudulent transactions is still missing. This paper studies one of the popular and experienced financial transaction services of Bangladesh, Bkash, to identify fraudulent text messages through the service. We propose an improved classification method for fraudulent messages through feature extraction and a hybrid deep learning (DL) approach with ML, which combines a convolutional neural network with sequential learning, referred to as the customized Bkash fraud semantic sequential learning (CBF-SSL) model. Seven machine-learning classifiers and five deep-learning sequential models are experimentally evaluated within the hybrid classifier. The performance of the fraud detection system is assessed using the loss function and confusion matrix on a dataset of 500 Bengali text messages. 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