Behavioral analysis of customer transaction patterns in financial fraud detection: an integrated machine learning approach

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dc.contributor.authorBalcıoğlu, Yavuz Selim
dc.contributor.authorMerter, Abdullah Kürşat
dc.contributor.authorÇelik, Beylem
dc.contributor.authorKarakaya, Turhan
dc.date.accessioned2025-09-26T11:06:05Z
dc.date.available2025-09-26T11:06:05Z
dc.date.issued2025
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, İşletme Bölümü
dc.description.abstractFinancial fraud detection has emerged as a critical challenge in the contemporary digital economy, with sophisticated fraudulent schemes continuously evolving to exploit vulnerabilities in financial systems. This study presents a comprehensive behavioral analysis of customer transaction patterns to enhance fraud detection capabilities through an integrated machine learning approach. Utilizing a dataset of 100 financial transactions encompassing diverse transaction types (purchases, transfers, withdrawals), customer profiles, and monetary values, we develop a multi-dimensional framework for identifying fraudulent activities. Our analysis reveals significant variations in fraud likeli-hood across transaction types, with transfer operations exhibiting the highest risk profile at 55%, while withdrawal transactions demon-strated no fraudulent activity. Furthermore, fraudulent transactions showed monetary values 28% higher than legitimate transactions, indi-cating distinct behavioral patterns. The study contributes to the literature by integrating behavioral finance theory with anomaly detection techniques, providing both theoretical insights and practical applications for financial institutions. Our findings demonstrate that customer behavioral patterns, transaction types, and monetary thresholds serve as robust predictors of fraudulent activity, with machine learning models achieving accuracy rates exceeding 94% while maintaining low false positive rates. The results have important implications for real-time fraud detection systems and risk management strategies in financial institutions.
dc.identifier.doi10.14419/s0r63575
dc.identifier.endpage44
dc.identifier.issn2227-5053
dc.identifier.issue5
dc.identifier.scopus2-s2.0-105015432008
dc.identifier.scopusqualityN/A
dc.identifier.startpage32
dc.identifier.urihttps://doi.org/10.14419/s0r63575
dc.identifier.urihttps://hdl.handle.net/11501/2386
dc.identifier.volume14
dc.indekslendigikaynakScopus
dc.institutionauthorÇelik, Beylem
dc.institutionauthorid0000-0003-4322-5907
dc.language.isoen
dc.publisherScience Publishing Corporation Inc.
dc.relation.ispartofInternational Journal of Basic and Applied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAnomaly Detection
dc.subjectBehavioral Analysis
dc.subjectCustomer Transaction Patterns
dc.subjectFinancial Fraud Detection
dc.subjectMachine Learning
dc.subjectRisk Management
dc.titleBehavioral analysis of customer transaction patterns in financial fraud detection: an integrated machine learning approach
dc.typeArticle

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