TY - JOUR T1 - Development of Card Fraud Detection Model with Intelligent Agent Technology and Use of Model Results in Prevention Processes TT - Akıllı Etmen Teknolojisi ile Kart Sahtekarlığı Tespit Modelinin Geliştirilmesi ve Model Sonuçlarının Önleme Süreçlerinde Kullanımı AU - Işık, Muhammed AU - Öztemel, Ercan PY - 2025 DA - August Y2 - 2025 JF - International Journal of Multidisciplinary Studies and Innovative Technologies JO - IJMSIT PB - SET Teknoloji WT - DergiPark SN - 2602-4888 SP - 125 EP - 128 VL - 9 IS - 1 LA - en AB - 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. KW - Card Fraud Detection KW - Intelligent Agent Technology KW - Double Deep Q Network KW - Card Fraud Prevention KW - Card Fraud Management System N2 - 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. CR - [1] Adhegaonkar, V. R., Thakur, A. R., & Varghese, N. (2024). Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods. 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 792–796. https://doi.org/10.1109/IDCIoT59759.2024.10467999 CR - [2] Işık, M. (2024). The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. International Journal of Advances in Engineering and Pure Sciences, 36(4), pp. 354–366. https://doi.org/10.7240/jeps.1519469 CR - [3] Işık, M., Sennaroğlu, B., & Genç, M. (2023). Predicting the Probability of Default with the Help of Macroeconomic Indicators in IFRS 9 Provision Calculations. In In Post Covid Era: Future of Economies and World Order, pp. 181–194. Istanbul University Press. https://iupress.istanbul.edu.tr/en/book/post-covid-era-future-of-economies-and-world-order/chapter/predicting-the-probability-of-default-with-the-help-of-macroeconomic-indicators-in-ifrs-9-provision-calculations CR - [4] Abdul Salam, M., Fouad, K. M., Elbably, D. L., & Elsayed, S. M. (2024). Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing and Applications, 36(11), pp. 6231–6256. https://doi.org/10.1007/s00521-023-09410-2 CR - [5] Oztemel, E., & Isik, M. (2025). A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection. Applied Sciences, 15(3), 1356. https://doi.org/10.3390/app15031356 CR - [6] Boutaher, N., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2020). A Review of Credit Card Fraud Detection Using Machine Learning Techniques. 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), pp. 1–5. https://doi.org/10.1109/CloudTech49835.2020.9365916 CR - [7] Boubker, M. B., Ouahabi, S., Elguemmat, K., & Eddaoui, A. (2021). A Comprehensive Study on Credit Card Fraud Prevention and Detection. 2021 Fifth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1–8. https://doi.org/10.1109/ICDS53782.2021.9626749 CR - [8] Razaque, A., Frej, M. B. H., Bektemyssova, G., Amsaad, F., Almiani, M., Alotaibi, A., Jhanjhi, N. Z., Amanzholova, S., & Alshammari, M. (2022). Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms. Applied Sciences, 13(1), 57. https://doi.org/10.3390/app13010057 CR - [9] Dar, H., Abbasi, A., & Naveed, A. (2020). Credit Card Fraud Prevention Planning using Fuzzy Cognitive Maps and Simulation. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 289–294. https://doi.org/10.1109/ICRITO48877.2020.9198002 CR - [10] King, S. T., Scaife, N., Traynor, P., Abi Din, Z., Peeters, C., & Venugopala, H. (2021). Credit Card Fraud Is a Computer Security Problem. IEEE Security & Privacy, 19(2), pp. 65–69. https://doi.org/10.1109/MSEC.2021.3050247 CR - [11] Balagolla, E. M. S. W., Fernando, W. P. C., Rathnayake, R. M. N. S., Wijesekera, M. J. M. R. P., Senarathne, A. N., & Abeywardhana, K. Y. (2021). Credit Card Fraud Prevention Using Blockchain. 2021 6th International Conference for Convergence in Technology (I2CT), pp. 1–8. https://doi.org/10.1109/I2CT51068.2021.9418192 CR - [12] Chumuang, N., Hiranchan, S., Ketcham, M., Yimyam, W., Pramkeaw, P., & Tangwannawit, S. (2020). Developed Credit Card Fraud Detection Alert Systems via Notification of LINE Application. 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pp. 1–6. https://doi.org/10.1109/iSAI-NLP51646.2020.9376829 CR - [13] Habibpour, M., Gharoun, H., Mehdipour, M., Tajally, A., Asgharnezhad, H., Shamsi, A., Khosravi, A., & Nahavandi, S. (2023). Uncertainty-aware credit card fraud detection using deep learning. Engineering Applications of Artificial Intelligence, 123, 106248. https://doi.org/10.1016/j.engappai.2023.106248 CR - [14] Cui, Y., Song, Z., & Hu, J. (2021). Esearch on Credit Card Fraud Classification Based on GA-SVM. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp. 1076–1080. https://doi.org/10.1109/AEMCSE51986.2021.00220 CR - [15] Tomar, P., Shrivastava, S., & Thakar, U. (2021). Ensemble Learning based Credit Card Fraud Detection System. 2021 5th Conference on Information and Communication Technology (CICT), pp. 1–5. https://doi.org/10.1109/CICT53865.2020.9672426 CR - [16] Dipta, D., Ibrahim, I., & Fukuta, N. (2022). Observing and Understanding Agent’s Characteristics with Environmental Changes for Learning Agents. 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 424–429. https://doi.org/10.1109/IIAIAAI55812.2022.00090 CR - [17] Efeoğlu, E. (2023). Site Classification using Feed Forward Backpropagation Artificial Neural Networks. International Journal of Multidisciplinary Studies and Innovative Technologies, 7(2), 41. https://doi.org/10.36287/ijmsit.7.2.1 UR - https://dergipark.org.tr/en/pub/ijmsit/issue//1714766 L1 - https://dergipark.org.tr/en/download/article-file/4938023 ER -