Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2218
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dc.contributor.authorRaina, Ajeet Singh-
dc.contributor.authorÇakar, Tuna-
dc.contributor.authorErtuğrul, Seyit-
dc.contributor.authorArslan, Şuayip-
dc.contributor.authorSayar, Alperen-
dc.date.accessioned2024-01-25T08:13:24Z-
dc.date.available2024-01-25T08:13:24Z-
dc.date.issued2023-
dc.identifier.citationSayar, A., Arslan, S., Raina, A. S., Ertugrul, S., & Cakar, T. (2023). GRAFRAUD: Fraud detection using graph databases and neural networks. In 2023 4th International Informatics and Software Engineering Conference. IEEE. pp. 1-4.en_US
dc.identifier.isbn9798350318036-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/2218-
dc.identifier.urihttps://doi.org/10.1109/IISEC59749.2023.10391038-
dc.descriptionIndex tarihi :19 Ocak 2024en_US
dc.description.abstractThe issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle Databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle Database to construct and deploy real-time systems that can efficiently detect fraudulent activities.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRedisgraphen_US
dc.subjectComplex data structuresen_US
dc.subjectFinancial transactionsen_US
dc.subjectDecision makingen_US
dc.subjectMachine learningen_US
dc.subjectHighperformanceen_US
dc.subjectFraud detectionen_US
dc.subjectReal-timeen_US
dc.subjectAnomaliesen_US
dc.subjectLarge-scale environmentsen_US
dc.subjectNeural networksen_US
dc.titleGrafraud: Fraud Detection Using Graph Databases and Neural Networksen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/IISEC59749.2023.10391038-
dc.identifier.scopus2-s2.0-85184668054en_US
dc.authoridTuna Çakar / 0000-0001-8594-7399-
dc.description.PublishedMonthKasımen_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US
dc.identifier.endpage4en_US
dc.identifier.startpage1en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.relation.journal2023 4th International Informatics and Software Engineering Conferenceen_US
dc.institutionauthorÇakar, Tuna-
item.grantfulltextembargo_20400101-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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