GenRL FinTech: Supporting the Risk Management Process through Reinforcement Intelligence

GenRL FinTech: Supporting the Risk Management Process through Reinforcement Intelligence | 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 GenRL FinTech: Supporting the Risk Management Process through Reinforcement Intelligence Rafsun Sheikh, Shah J Miah, Peter Cook This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7190065/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 10 You are reading this latest preprint version Abstract Bringing technical innovations to managing financial risks has been a significant issue for managers in FinTech (financial technologies) organizations. Although FinTech organizations always explore to find new methods of Financial Risk Management (FRM), specifically for achieving smooth governance, common issues exist with time-consuming and labor-intensive processes that require adequate computational support. Previous AI (artificial intelligence) driven approaches in FRM do not fully support critical computational provisions for regulatory compliance. To address the issues, utilizing a design science research paradigm, this paper introduces a new innovative generative AI framework called ‘ GenRL’ (Generative Reinforcement Learning) , as an innovative computational FRM model grounded in Reinforcement Learning (RL). The GenRL artifact is a prototype featuring multiple GenAI agents that autonomously acquire and refine domain-specific expertise in FinTech regulatory compliance. Our evaluation demonstrates that GenRL enhances the efficiency of compliance officers, particularly in terms of the accuracy of FRM decision-making. Generative Artificial Intelligence Reinforcement Learning Financial Risk Management Financial Governance Regulatory Compliance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 06 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 22 Jul, 2025 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-7190065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513242417,"identity":"4c4d236a-7746-4eac-94e1-0b445f792a29","order_by":0,"name":"Rafsun Sheikh","email":"","orcid":"","institution":"University of Newcastle Australia","correspondingAuthor":false,"prefix":"","firstName":"Rafsun","middleName":"","lastName":"Sheikh","suffix":""},{"id":513242418,"identity":"5ec678a4-691c-4ccf-a15f-9bd5bb2abac6","order_by":1,"name":"Shah J Miah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2OMUvDUBDH73GQLCddnxSar/BCIRFE81USMnRJHLtYNFDQJR8g0C+RKeCWEkiW0DmiQ0RwE9pFBBVMqps+zOjwfnA33N2P+wMoFP8RDtq+Cz36nuh904YolH8NCIcpAIK7AxVjtXxqWXBk2IfP60danDoOYg7beQGjxP1VEfelLVjGzZvVmT+l0vdi1FyWbArgjUThrsU7haV3gTUOI3QJSeDBVQEgUYxk9tIrTnpb229hdOkQjrb40SmGRIEm2H/x0oYsDKOCxUiArFOELFgTzLmXcT+tg+n4vay8uNDEOt7MyKxbWbCM77KLk7SqzV2yOHf06+VD+zo/nkwqSbCeH6u8K5LfKxQKheIvPgGdYFiowH+kwAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Newcastle Australia","correspondingAuthor":true,"prefix":"","firstName":"Shah","middleName":"J","lastName":"Miah","suffix":""},{"id":513242419,"identity":"297a3659-bbdb-428c-817a-5c25d7d38e96","order_by":2,"name":"Peter Cook","email":"","orcid":"","institution":"Novatti Group, Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Cook","suffix":""}],"badges":[],"createdAt":"2025-07-22 19:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7190065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7190065/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-026-01133-2","type":"published","date":"2026-03-27T16:12:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105755037,"identity":"6834b3d4-f8ac-49bc-a180-75a10c0f3070","added_by":"auto","created_at":"2026-03-30 16:24:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":700425,"visible":true,"origin":"","legend":"","description":"","filename":"GenRLFinTechVersion3.9withauthorsdetails1Aug2025SM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7190065/v1_covered_669022a8-ee47-43f2-af5a-27466fda29b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GenRL FinTech: Supporting the Risk Management Process through Reinforcement Intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generative Artificial Intelligence, Reinforcement Learning, Financial Risk Management, Financial Governance, Regulatory Compliance","lastPublishedDoi":"10.21203/rs.3.rs-7190065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7190065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBringing technical innovations to managing financial risks has been a significant issue for managers in FinTech (financial technologies) organizations. 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