Seher ARSLANKAYA, Muhammed Taha ZEREN, Yasin KIRELLİ

Veri Madenciliği ile E-Ticarette Kredi Kartı Dolandırıcılığının Tespiti

Kredi kartı ile ödeme, e-ticaret sitelerinin en çok tercih edilen yöntemlerinden biridir. Dolandırıcılık şüphesi olan siparişler, alışverişsiteleri için en büyük endişe kaynağıdır. Sahtekarlık işlemleri sadece müşterileri değil, aynı zamanda şirketleri ve bankaları da etkiler.Bu nedenle, şirketler siparişleri sınıflandırabilmeli ve şüpheli işlemlere karşı önlemler alabilmelidir. Bankacılık tarafında, müşterilerhakkında daha fazla bilgi olması nedeniyle sınıflandırma daha kolaydır, ancak bu süreci e-ticaret sitelerinde belirlemek daha zordur. Buçalışmada, özel bir e-ticaret girişiminin gerçek sipariş verileri incelenmiş ve şüpheli işlemler belirlenmiştir. Öncelikle, tüm siparişverileri analiz edilir ve filtrelenir. Sınıflandırma için en iyi değişkenler değişken seçim algoritmaları ile belirlenmiştir. Daha sonrasınıflandırma algoritmaları uygulanır ve %92 başarı oranı ile şüpheli siparişler belirlenir. Karşılaştırmalı veri madenciliği yöntemleriolarak Naive Bayesian, Karar Ağaçları ve Yapay Sinir Ağı kullanılmıştır.

Detection of Credit Card Fraud in E-Commerce Using Data Mining

Credit card payment is one of the most preferred methods of e-commerce sites. Fraud orders are the biggest concerns for online shoppingsites. Fraud operations affect not only customers but also companies and banks. Hence, companies should be able to classify orders andtake 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-commerceenterprise has been examined and suspicious transactions are determined. First of all, all order data is analyzed and filtered. The bestvariables for classification are determined by variable selection algorithms. Afterwards, classification algorithms are applied andsuspicious orders are determined with 92% success rate. Naïve Bayesian, Decision Trees and Artificial Neural Network have been usedas comparative data mining methods.

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