Algorithmic Compliance and Regulatory Loss in Digital Assets

Algorithmic Compliance and Regulatory Loss in Digital Assets | 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 Algorithmic Compliance and Regulatory Loss in Digital Assets Krishna Sharma, Khem Raj Bhatt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8936868/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 We study the deployment performance of machine learning--based enforcement systems used in cryptocurrency anti-money laundering (AML). Using forward-looking and rolling evaluations on Bitcoin transaction data, we show that strong static classification metrics substantially overstate real-world regulatory effectiveness. Temporal non-stationarity induces pronounced instability in cost-sensitive enforcement thresholds, generating large and persistent excess regulatory losses relative to dynamically optimal benchmarks. The core failure arises from miscalibration of decision rules rather than from declining predictive accuracy per se. These findings underscore the fragility of fixed AML enforcement policies in evolving digital asset markets and motivate loss-based evaluation frameworks for regulatory oversight. Cryptocurrency Anti-Money Laundering Concept Drift Regulatory Technology Model Risk Full Text Additional Declarations No competing interests reported. Supplementary Files AlgorithmicComplianceandRegulatoryLossinDigitalAssets.pdf 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-8936868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600116850,"identity":"a547de07-f0b7-4763-b3ce-2a91fed646cc","order_by":0,"name":"Krishna Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3NsYrCMBzH8ZQ/dIrtmhDwGQKFQLH0WQKBTsVzPW7Q4gN09eAeQhAyp3RwUbpWnORWN0FObrHqKsFuDvlC+EHIhyDkcr1hBOH7puF9ku6Y2+0LRNEZdJP1IN7SvEpoua1On3kKUdNUp4lMPgID1R5bCCNjRbdaBaJVwBYyi7+Nr0Y2MiSY00IDiBYQG/zVnBssmJWEm+hS6Km3Kmv4x/JGwrOVMJSL7pfaWyLlswfBvpXQRS7iQq8VaZUYYZlxWvtR/GMhpNlEu0J/pWFZ/e6xTHiwnh/ao4U8Cfo9d7lcLteTrsaIRzJZ1jRPAAAAAElFTkSuQmCC","orcid":"","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Krishna","middleName":"","lastName":"Sharma","suffix":""},{"id":600116851,"identity":"6d45951e-3974-4677-89c9-a50924c544ba","order_by":1,"name":"Khem Raj Bhatt","email":"","orcid":"","institution":"First Citizens Bank","correspondingAuthor":false,"prefix":"","firstName":"Khem","middleName":"Raj","lastName":"Bhatt","suffix":""}],"badges":[],"createdAt":"2026-02-22 04:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8936868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8936868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960773,"identity":"1ae46cf9-28cd-4e9f-90d0-3b57a62d2f96","added_by":"auto","created_at":"2026-04-15 09:23:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2945599,"visible":true,"origin":"","legend":"","description":"","filename":"AlgorithmicComplianceandRegulatoryLossinDigitalAssets..pdf","url":"https://assets-eu.researchsquare.com/files/rs-8936868/v1_covered_8daee7e3-27a3-409b-99fd-cf47f84e9249.pdf"},{"id":103990593,"identity":"dc23a020-9f34-4e74-86a8-9e11e932f0b0","added_by":"auto","created_at":"2026-03-05 11:29:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36815,"visible":true,"origin":"","legend":"","description":"","filename":"AlgorithmicComplianceandRegulatoryLossinDigitalAssets.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8936868/v1/844960f7befd5cfb0c10537e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Algorithmic Compliance and Regulatory Loss in Digital Assets","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":"Cryptocurrency, Anti-Money Laundering, Concept Drift, Regulatory Technology, Model Risk","lastPublishedDoi":"10.21203/rs.3.rs-8936868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8936868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We study the deployment performance of machine learning--based enforcement\nsystems used in cryptocurrency anti-money laundering (AML). 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