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

Year 2025, Volume: 9 Issue: 1, 125 - 128, 31.07.2025

Ercan Öztemel , Muhammed Işık

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

Ethical Statement

The authors declare that this study complies with Research and Publication Ethics.

Supporting Institution

TÜBİTAK

Thanks

We would like to thank the Scientific and Technological Research Council of Turkey (TÜBİTAK) for their support in this paper, by means of the 2211 (C) National Doctoral Scholarship Program.

References

• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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
• [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