Gini calculation and rule performance interpretation

Gini calculation and rule performance interpretation | 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PDF Method Article Gini calculation and rule performance interpretation Meenal Badki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4822777/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The Gini coefficient is a widely used measure of income inequality within a population. This paper investigates the application of the Gini coefficient concept to calculate fraud risk based transaction amount losses. The paper is essentially divided into two parts: (1) Interpretation and implementation of Lorenz curve in calculating fraud risk in a real-time scenario for online e-commerce merchants/customers for various industries. Implementing Gini is useful for verifying fraud-based metrics or saving fraud losses after a machine learning model has been implemented and deployed in production. (2) The rules automation system is helpful in optimizing and improving strategies required for deployment on a real-time transaction based platform in the financial services world where the real-time decisioning system for a customer on board plays an important role. The study concludes that Gini coefficient remains a robust tool for evaluating risk based losses, thus helping financial institutions make informed strategy-driven decisions, and further also explores the business rule sets that help to optimize the strategic decisions. model metrics rule performance Gini index accuracy ratio Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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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-4822777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":333498373,"identity":"2533a8ae-e361-4660-b07f-e73f47456ca2","order_by":0,"name":"Meenal Badki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYNCCghoefgnmA0CWhAyRWgyOyUjOYEsAaeEhVguzjcENHgMQk7AW3fbmYw9+GLDxMNzu+fzqRo0FDwP74aMb8GkxO3Ms3bDHQIaHcc7ZbdY5x4AO40lLu4FXy40cMwkeoC3MErnbjHPYgFokeMzwa7n//pvkHwNmHjaJnGfGOf+I0XKDh02aB6iFRyKH+XFuGzFazqSZScsYHOORkDlmxpzbJ8HDRtAvxw8/k3xTUWNvf7v58eecb3Vy/OyHj+HVggzYJMAkscpBgPkDKapHwSgYBaNg5AAAOlxB+6crY28AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Meenal","middleName":"","lastName":"Badki","suffix":""}],"badges":[],"createdAt":"2024-07-29 14:27:54","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4822777/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4822777/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83517152,"identity":"02c68757-4a9a-48ce-bbf9-9404fc24bc8e","added_by":"auto","created_at":"2025-05-27 18:57:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2160543,"visible":true,"origin":"","legend":"","description":"","filename":"Ginicalculationandruleperformanceinterpretationupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4822777/v2_covered_ee88103a-a5c9-4840-a842-78714f698d6a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Gini calculation and rule performance interpretation","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":"model metrics, rule performance, Gini index, accuracy ratio","lastPublishedDoi":"10.21203/rs.3.rs-4822777/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4822777/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Gini coefficient is a widely used measure of income inequality within a population. 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