Development of machine learning based fraud detection models for credit cards

Abstract

In today's global world, technology is rapidly developing and this can cause more risks, especially in sectors such as banking. Fraudsters create security vulnerabilities with many new techniques. Various approaches have emerged to prevent these vulnerabilities, but these approaches are generally inadequate due to reasons such as high data volume, multiple institutions, channels (mobile applications, websites, call centers) and fraudulent activities between locations. In this context, machine learning-based systems gain importance due to their dynamic structure. In this study, it is aimed to develop a model that provides fraudulent transaction detection using the Random Forest (RF) classifier. Docker and Kubernetes have been used for model distribution in the study. The performance of the developed model has been evaluated with Accuracy, Precision, Recall and F1 Score. With the developed fraud detection model, an Accuracy value of 0.771 has been achieved.

Keywords

• Fraud Detection
• Machine Learning
• Random Forest

Kredi kartları için makine öğrenimi tabanlı dolandırıcılık tespit modellerinin geliştirilmesi

Öz

Günümüzün küresel dünyasında teknoloji hızla gelişmekte ve bu durum özellikle bankacılık gibi sektörlerde daha fazla risk oluşturabilmektedir. Dolandırıcılar birçok yeni teknikle güvenlik açıkları oluşturmaktadır. Bu açıkları önlemek için çeşitli yaklaşımlar ortaya çıkmıştır ancak bu yaklaşımlar yüksek veri hacmi, birden fazla kurum, kanal (mobil uygulamalar, web siteleri, çağrı merkezleri) ve lokasyonlar arası dolandırıcılık faaliyetleri gibi nedenlerden dolayı genellikle yetersiz kalmaktadır. Bu bağlamda dinamik yapıları nedeniyle makine öğrenmesi tabanlı sistemler önem kazanmaktadır. Bu çalışmada, Random Forest (RF) sınıflandırıcısı kullanılarak dolandırıcılık işlem tespiti sağlayan bir model geliştirilmesi hedeflenmiştir. Çalışmada model dağılımı için Docker ve Kubernetes kullanılmıştır. Geliştirilen modelin performansı Accuracy, Precision, Recall ve F1 Score ile değerlendirilmiştir. Geliştirilen dolandırıcılık tespit modeli ile 0,771'lik bir Accuracy değeri elde edilmiştir.

Anahtar Kelimeler

• Dolandırıcılık Tespiti
• Makine Öğrenmesi
• Rastgele Orman

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