Year 2020, Volume , Issue 20, Pages 522 - 529 2020-12-31

Veri Madenciliği ile E-Ticarette Kredi Kartı Dolandırıcılığının Tespiti
Detection of Credit Card Fraud in E-Commerce Using Data Mining

Yasin KIRELLİ [1] , Seher ARSLANKAYA [2] , Muhammed Taha ZEREN [3]


Kredi kartı ile ödeme, e-ticaret sitelerinin en çok tercih edilen yöntemlerinden biridir. Dolandırıcılık şüphesi olan siparişler, alışveriş siteleri için en büyük endişe kaynağıdır. Sahtekarlık işlemleri sadece müşterileri değil, aynı zamanda şirketleri ve bankaları da etkiler. Bu nedenle, şirketler emirleri sınıflandırabilmeli ve şüpheli işlemlere karşı önlemler alabilmelidir. Bankacılık tarafında, müşteriler hakkında daha fazla bilgi olması nedeniyle sınıflandırma daha kolaydır, ancak bu süreci e-ticaret sitelerinde belirlemek daha zordur. Bu çalışmada, özel bir e-ticaret girişiminin gerçek sipariş verileri incelenmiş ve şüpheli işlemler belirlenmiştir. Öncelikle, tüm sipariş verileri analiz edildi ve filtrelendi. Sınıflandırma için en iyi değişkenler değişken seçim algoritmaları ile belirlenmiştir. Daha sonra sınıflandırma algoritmaları uygulanmış ve %92 başarı oranı ile şüpheli siparişler belirlenmiştir. Karşılaştırmalı veri madenciliği yöntemleri olarak Naive Bayesian, Karar Ağaçları ve Yapay Sinir Ağı kullanılmıştır.
Credit card payment is one of the most preferred methods of e-commerce sites. Fraud orders are the biggest concerns for online shopping sites. Fraud operations affect not only customers but also companies and banks. Hence, companies should be able to classify orders and take measures against suspicious transactions. Classification is easier on the banking side because of more information about customers, but it is more difficult to determine this process on e-commerce sites. In this study, the actual order data of a private e-commerce enterprise was examined and suspicious transactions were determined. First of all, all order data was analyzed and filtered. The best variables for classification were determined by variable selection algorithms. Afterwards, classification algorithms were applied and suspicious orders were determined with 92% success rate. Naïve Bayesian, Decision Trees and Artificial Neural Network were used as comparative data mining methods.
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Primary Language en
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-3605-8621
Author: Yasin KIRELLİ (Primary Author)
Institution: SAKARYA UNIVERSITY
Country: Turkey


Orcid: 0000-0001-6023-2901
Author: Seher ARSLANKAYA
Institution: SAKARYA ÜNİVERSİTESİ
Country: Turkey


Orcid: 0000-0001-5615-0751
Author: Muhammed Taha ZEREN
Institution: TAI – Turkish Aerospace Industries
Country: Turkey


Dates

Publication Date : December 31, 2020

APA Kırelli̇, Y , Arslankaya, S , Zeren, M . (2020). Detection of Credit Card Fraud in E-Commerce Using Data Mining . Avrupa Bilim ve Teknoloji Dergisi , (20) , 522-529 . DOI: 10.31590/ejosat.747399