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

Kaynakça

1. Baisholan, N., Dietz, J. E., Gnatyuk, S., Turdalyuly, M., Matson, E. T., & Baisholanova, K. (2025). FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets. Computers, 14(4), 120.
2. Bhalala, R. B., & Patel, N. (2025). Machine Learning based Credit Card Fraud Detection Model. IJFRI, 1(1).
3. Bonde, L., & Bichanga, A. K. (2025). Improving Credit Card Fraud Detection with Ensemble Deep Learning-Based Models: A Hybrid Approach Using SMOTE-ENN. Journal of Computing Theories and Applications, 2(3), 384.
4. Hemanth, K., Virat, K. S., Rohith, M. D., Reddy, K. V. P., & Selv, A. S. (2025). Credit Card Fraud Detection using Machine Learning Methods. In 2025 Emerging Technologies for Intelligent Systems (ETIS), IEEE, pp. 1-6.
5. Mousa, M. A. M. (2025). Credit Card Fraud Detection in the Banking Sector: A Comprehensive Machine Learning Approach for Information Security. Artificial Intelligence in Cybersecurity, 2, pp. 1-13.
6. Nair, S. S., Lakshmikanthan, G., Belagalla, N., Belagalla, S., Ahmad, S. K., & Farooqi, S. A. (2025). Leveraging AI and Machine Learning for Enhanced Fraud Detection in Digital Banking System: A Comparative Study. In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), IEEE, pp. 1278-1282.
7. Nijanthan, V., Muthukumaran, N., Pratheeshba, B., & Riyas Ahamed, M. (2025). The Impact of Machine Learning Algorithms on Credit Card Fraud Detection: A Comparative Study. In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV), IEEE, pp. 1576-1580.
8. Sultana, I., Maheen, S. M., Kshetri, N., & Zim, M. N. F. (2025). detectGNN: Harnessing Graph Neural Networks for Enhanced Fraud Detection in Credit Card Transactions. arXiv preprint arXiv:2503.22681.

9. Vivek, Y., Ravi, V., Mane, A., & Naidu, L. R. (2025). Explainable One Class Classification for ATM Fraud Detection. In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS), IEEE, pp. 114-119.
10. Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of Machine Learning-Based K-Means Clustering for Financial Fraud Detection. Academic Journal of Science and Technology, 10(1), pp. 33-39.
11. Raikar, M. M., Patil, P., Guggari, S., Shavi, P., Mudavi, S., Patil, N., & Rangannavar, V. (2024). Leveraging Docker Containers for Deployment of Web Applications in Microservices Architecture. In 2024 First International Conference for Women in Computing (InCoWoCo), IEEE, pp. 1-6.
12. Yang, G. (2024). Credit Card Fraud Detection Based on Machine Learning Prediction. In 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024), Atlantis Press, pp. 35-45.
13. Yu, C., Xu, Y., Cao, J., Zhang, Y., Jin, Y., & Zhu, M. (2024). Credit card fraud detection using advanced transformer model. In 2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom), IEEE, pp. 343-350.
14. Ali, A. A., Khedr, A. M., El-Bannany, M., & Kanakkayil, S. (2023). A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique. Applied Sciences, 13(4), 2272.
15. Chang, Y. T., & Fan, N. H. (2023). A novel approach to market segmentation selection using artificial intelligence techniques. The Journal of Supercomputing, 79(2), pp. 1235-1262.
16. Jiang S, Dong R, Wang J, Xia M. (2023). Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network. Systems, 11(6):305.
17. Rathor, K., Vidya, S., Jeeva, M., Karthivel, M., Ghate, S. N., & Malathy, V. (2023). Intelligent System for ATM Fraud Detection System using C-LSTM Approach. In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, pp. 1439-1444.
18. Vivek, Y., Ravi, V., Mane, A. A., & Naidu, L. R. (2023). ATM fraud detection using streaming data analytics. arXiv preprint arXiv:2303.04946.
19. Zhang, R., Cheng, Y., Wang, L., Sang, N., & Xu, J. (2023). Efficient Bank Fraud Detection with Machine Learning. Journal of Computational Methods in Engineering Applications, 1-10.
20. Alamri M, Ykhlef M. (2022). Survey of Credit Card Anomaly and Fraud Detection Using Sampling Techniques. Electronics, 11(23):4003.
21. Mahdi Rezapour, (2019), Anomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study, International Journal of Advanced Computer Science and Applications(IJACSA), 10(11).
22. Park, S., & Kim, J. (2019). Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Applied Sciences, 9(5), 942.