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Explainable Machine Learning for Real-Time Payment Fraud Detection: Building Trustworthy Models to Protect Financial Transactions

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dc.citedby1
dc.contributor.authorAl-hchaimi A.A.J.en_US
dc.contributor.authorAlomari M.F.en_US
dc.contributor.authorMuhsen Y.R.en_US
dc.contributor.authorSulaiman N.B.en_US
dc.contributor.authorAli S.H.en_US
dc.contributor.authorid57219174675en_US
dc.contributor.authorid57350402200en_US
dc.contributor.authorid57216731867en_US
dc.contributor.authorid35726273200en_US
dc.contributor.authorid59236367500en_US
dc.date.accessioned2025-03-03T07:46:40Z
dc.date.available2025-03-03T07:46:40Z
dc.date.issued2024
dc.description.abstractIn this study, we introduce an advanced machine learning model integrated with explainable AI techniques to enhance the detection of payment fraud in real-time scenarios within the digital finance sector. As online transactions continue to proliferate, so too do the fraudulent activities associate with them. Our approach effectively differentiates between legitimate and fraudulent transactions by meticulously analyzing key features such as transaction amount, type, and the accounts involved. Through a comprehensive evaluation of various machine learning models, the Decision Tree model emerged as the most effective, achieving an accuracy of 95.4048%, precision of 92.9461%, recall of 98.2456%, and an F1-score of 95.5224%. This study not only proposes a robust and explainable machine learning framework but also significantly enhances the transparency of fraud detection decisions. It equips financial institutions with a potent tool to safeguard their customers? assets against fraud, thereby bolstering the reliability and trustworthiness of digital payment systems. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-63717-9_1
dc.identifier.epage25
dc.identifier.scopus2-s2.0-85199772810
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199772810&doi=10.1007%2f978-3-031-63717-9_1&partnerID=40&md5=fc5b238cb824678928c0ffd98f7a6cc5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37019
dc.identifier.volume1033 LNNS
dc.pagecount24
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectCrime
dc.subjectDecision trees
dc.subjectE-learning
dc.subjectMachine learning
dc.subjectNetwork security
dc.subjectAI techniques
dc.subjectDecision-tree model
dc.subjectDigital finance security
dc.subjectFinancial transactions
dc.subjectFraud detection
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectReal- time
dc.subjectReal-time fraud analyse
dc.subjectTransaction analyse
dc.subjectFinance
dc.titleExplainable Machine Learning for Real-Time Payment Fraud Detection: Building Trustworthy Models to Protect Financial Transactionsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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