Akıllı Etmen Teknolojisi ile Kart Sahtekarlığı Tespit Modelinin Geliştirilmesi ve Model Sonuçlarının Önleme Süreçlerinde Kullanımı

Abstract

Değişen ihtiyaçlar ve kullanım kolaylığı, bireysel ve kurumsal segmentlerde kredi kartı ürünlerine olan talebi artırmaktadır. Kredi kartlarına olan bu eğilim, kart dolandırıcılığı vakalarında artışı da beraberinde getirmektedir. Bu istenmeyen durumlar, özellikle bankalarda nakit ve itibar risklerine neden olmaktadır. Bankalarda sıklıkla karşılaşılan dolandırıcılık vakalarının önlenmesi için tespit ve önleme çalışmaları yapılmaktadır. Bu çalışmanın ilk aşamasında, iyi bilinen sınıflandırma (Rastgele Orman ve Destek Vektör Makinesi) tespit modellerine ek olarak, Çift Derin Q Ağı gibi etmen tabanlı modeller de geliştirilmiştir. Model geliştirme sürecinden sonra, kart dolandırıcılığı tespit modelinin çıktıları kullanılarak önleme süreci oluşturulmuştur. Bu çalışmanın temel amacı, kart dolandırıcılığı tespitinde etmen teknolojisini kullanmak ve önleme çalışmaları için yeni bir aksiyon akışı oluşturmaktır. Bu şekilde, kart dolandırıcılığı davranışını tespit edebilen, değerlendirebilen ve önleyebilen etmen destekli bir yapı geliştirilmiştir. Tespit modeline ek olarak, bir önleme süreci geliştirerek kart dolandırıcılığı yönetim sistemini desteklemeye çalışılmıştır.

Keywords

• Kart Sahtekarlığı Tespiti
• Zeki Etmen Teknolojisi
• Çift Derin Q Ağı
• Kart Sahtekarlığı Önleme
• Kart Sahtekarlığı Yönetim Sistemi

Development of Card Fraud Detection Model with Intelligent Agent Technology and Use of Model Results in Prevention Processes

Abstract

Changing needs and ease of use are increasing the demand for credit card products in the individual and corporate segments. This tendency towards credit cards brings with it an increase in card fraudster cases. These undesirable situations cause cash and reputational risks, especially in banks. Detection and prevention efforts are being made to prevent fraud cases that are frequently encountered in banks. In the first stage of this paper, in addition to well-known classification (Random Forest and Support Vector Machine) detection models, agent-based models such as Double Deep Q Network have been developed. After the model development process, the outputs of the card fraud detection model were used to create the prevention process. The main purpose of this paper is to use agent technology in card fraud detection and to create a new action flow for prevention efforts. In this way, an agent-supported structure that can detect, evaluate and prevent card fraud behavior has been developed. In addition to the detection model, the study also attempted to support the card fraud management system by developing a prevention process.

Keywords

• Card Fraud Detection
• Intelligent Agent Technology
• Double Deep Q Network
• Card Fraud Prevention
• Card Fraud Management System

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