Review of Fraud Detection Methods and Development of a Data Mining Technique for Real-Time Financial Fraud Detection.

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dc.contributor.authorOgude, Ufuoma
dc.contributor.authorNwohiri, Anthony
dc.contributor.authorUgbaja, Geraldine
dc.date.accessioned2022-08-30T20:29:10Z
dc.date.available2022-08-30T20:29:10Z
dc.date.issued2022
dc.description.abstractFraud that involves cell phones, insurance claims, tax return claims, credit card transactions, government procurement etc. represent significant problems for governments and businesses. Technology advances have brought along new opportunities and security challenges. Due to the dramatic increase in fraud which has cost businesses billions of dollars each year, several modern fraud detection techniques are continually evolving to meet the unprecedented challenge. Data mining (DM) is the most recognized and effective technology that has been deployed for fraud detection. This study looks at the concept of DM and current techniques used in detecting fraud and reviews DM methods used to detect fraudulent payment transactions. It explores some of the most effective DM techniques for detecting different types of fraud, categorizing them based on supervised and unsupervised methods. An efficient model that can differentiate fraudulent transactions from genuine transactions based on a given dataset is proposed.en_US
dc.identifier.citationOgude, U.C., Nwohiri, A.M., Ugbaja, G.U. (2022). Review of Fraud Detection Methods and Development of a Data Mining Technique for Real-Time Financial Fraud Detection. Bayero Journal of Engineering and Technology (BJET), 17 (1), 1-11en_US
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/11144
dc.language.isoen_USen_US
dc.publisherBayero University Kanoen_US
dc.relation.ispartofseries17;1
dc.subjectFraud Detection; Data Mining Techniques; Financial Fraud; Supervised Learning; K-Means;en_US
dc.titleReview of Fraud Detection Methods and Development of a Data Mining Technique for Real-Time Financial Fraud Detection.en_US
dc.typeArticleen_US
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