Detection of fraudulent transactions using artificial neural networks and decision tree methods

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dc.contributor.authorIşık, Yusuf
dc.contributor.authorKefe, İlker
dc.contributor.authorSağlar, Jale
dc.date.accessioned2024-09-19T16:21:02Z
dc.date.available2024-09-19T16:21:02Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThe accounting systems generate a large amount of data due to financial transactions. Intentionally fraudulent transactions can occur in high-dimensional and large numbers of emerging data. While many methods can be used for the estimation and detection of fraudulent transactions in accounting, which differ in the audit process, scope and application method, data mining methods can also be used today due to a large number of data and the desire not to narrow the scope of the audit. This study tested the accuracy of detecting fraudulent transactions using artificial neural networks and decision tree methods. According to the results of the analysis test data set for detecting fraud or error risk, 99.7981% accuracy was obtained in the artificial neural networks method and 99.9899% in the decision tree method.en_US
dc.identifier.doi10.15295/bmij.v11i2.2200
dc.identifier.endpage467en_US
dc.identifier.issn2148-2586
dc.identifier.issue2en_US
dc.identifier.startpage451en_US
dc.identifier.trdizinid1185441en_US
dc.identifier.urihttps://doi.org/10.15295/bmij.v11i2.2200
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1185441
dc.identifier.urihttps://hdl.handle.net/20.500.12483/15541
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofBusiness and Management Studies: An International Journalen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleDetection of fraudulent transactions using artificial neural networks and decision tree methodsen_US
dc.typeArticleen_US

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