AI Agent Adoption in Banking A Quantitative Analysis with Focus on Bangladeshi Customer Services

AI Agent Adoption in Banking A Quantitative Analysis with Focus on Bangladeshi Customer Services | 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 AI Agent Adoption in Banking A Quantitative Analysis with Focus on Bangladeshi Customer Services Mohammad Abdullah-Al-Kafe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8892976/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 examines determinants of AI agent adoption in banking through secondary data analysis of 35 Bangladeshi commercial banks over 2020–2024, complemented by global comparative benchmarking. Using panel data regression and the Technology-Organization-Environment (TOE) framework, we analyze 175 bank-year observations to test five hypotheses on adoption drivers. Results indicate cost-to-income ratio (β = -0.124, p < 0.01), organizational size (β = 0.782, p < 0.001), and digital maturity (β = 0.095, p < 0.001) significantly predict AI adoption intention, explaining 68% of variance (Pseudo R² = 0.68). Financial modeling projects sector-wide cost savings of BDT 1,600-2,300 crore annually with 14-21-month payback periods. However, infrastructure gaps (72% vs. 94% 4G coverage compared to developed markets) and human capital constraints (60–70% specialist shortage) moderate implementation feasibility. For Bangladeshi banks, findings suggest phased implementation prioritizing high-volume, low-complexity use cases yields optimal ROI (141–261% over three years). This study contributes to emerging market technology adoption literature by demonstrating cost drivers dominate in resource-constrained environments (r = 0.78), contrasting with competitive pressure primacy in developed markets (r = 0.68). Theoretical contributions include extending TOE framework application to South Asian contexts and introducing digital maturity as a significant mediating variable in AI adoption pathways. Artificial Intelligence and Machine Learning Development Economics AI adoption banking automation Technology-Organization-Environment framework Bangladesh emerging markets panel data analysis financial technology Digital banking 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. 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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-8892976","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592122389,"identity":"0d23761e-ea85-418b-9cfa-0dc6680f55cc","order_by":0,"name":"Mohammad Abdullah-Al-Kafe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACgwOMDxg+/LCRA3EOPCBGi2EDswHjzJ40Y7CWBGK0GDMwGzDzsB1KbADxiNJixn6Y8TEPz4H0+WGHHwJtsZPTbSCgxYYnmdlwjsWd3I230wyAWpKNzQ4Q0sKQf0ziDc+z3I2zE0BaDiRuI6TFjP8x+w8etsPphrPTPxCnxVgimY0RqCVBXjqHSFsMZzxmlgQGsuEG6ZyCAwkGRPjF4Hwy4wdgVMrLz07f/OFDhZ0cQS0IvWCVBsQqBwH5BlJUj4JRMApGwYgCAF43SJlXoXwkAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-7699-837X","institution":"Mutual Trust Bank PLC","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Abdullah-Al-Kafe","suffix":""}],"badges":[],"createdAt":"2026-02-16 11:58:46","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-8892976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8892976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963176,"identity":"d06cb741-3577-456b-b636-a45261d86a0a","added_by":"auto","created_at":"2026-02-19 04:14:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":821499,"visible":true,"origin":"","legend":"","description":"","filename":"AIAgentAdoptioninBankingAQuantitativeAnalysiswithFocusonBangladeshiCustomerServices6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8892976/v1_covered_5eedd844-f210-4b14-8cca-2707992335a3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAI Agent Adoption in Banking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Quantitative Analysis with Focus on Bangladeshi Customer Services\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":"AI adoption, banking automation, Technology-Organization-Environment framework, Bangladesh, emerging markets, panel data analysis, financial technology, Digital banking","lastPublishedDoi":"10.21203/rs.3.rs-8892976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8892976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines determinants of AI agent adoption in banking through secondary data analysis of 35 Bangladeshi commercial banks over 2020\u0026ndash;2024, complemented by global comparative benchmarking. Using panel data regression and the Technology-Organization-Environment (TOE) framework, we analyze 175 bank-year observations to test five hypotheses on adoption drivers. Results indicate cost-to-income ratio (β = -0.124, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), organizational size (β\u0026thinsp;=\u0026thinsp;0.782, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and digital maturity (β\u0026thinsp;=\u0026thinsp;0.095, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly predict AI adoption intention, explaining 68% of variance (Pseudo R\u0026sup2; = 0.68). Financial modeling projects sector-wide cost savings of BDT 1,600-2,300 crore annually with 14-21-month payback periods. However, infrastructure gaps (72% vs. 94% 4G coverage compared to developed markets) and human capital constraints (60\u0026ndash;70% specialist shortage) moderate implementation feasibility. For Bangladeshi banks, findings suggest phased implementation prioritizing high-volume, low-complexity use cases yields optimal ROI (141\u0026ndash;261% over three years). This study contributes to emerging market technology adoption literature by demonstrating cost drivers dominate in resource-constrained environments (r\u0026thinsp;=\u0026thinsp;0.78), contrasting with competitive pressure primacy in developed markets (r\u0026thinsp;=\u0026thinsp;0.68). 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