NEURAL NETWORKS IN THE TASK OF RECOGNIZING CREDIT CARD FRAUD
The problem of recognizing credit card fraud is currently relevant due to the significant increase in the use of credit cards by the population. At the same time, the methods and algorithms used by credit card companies are far from perfect. Machine learning methods and algorithms are currently being used to solve this problem. The paper presents some current research being conducted in this area. This paper presents a study on the use of a neural network for credit card fraud detection. The availability of publicly available training datasets and the challenges of configuring a neural network based on organizational policies are discussed. It demonstrates ways to tune the neural network under consideration to better recognize fraudulent transactions as such, while observing a greater number of legitimate transactions classified as fraudulent, and vice versa. It also demonstrates ways to tune the neural network to minimize the classification of legitimate transactions as fraudulent, while observing the omission of fraudulent transactions.
Abramov K.V., Balabanova T.N., Belov A.S., Novikov A.G. Neural Networks in the Task of Recognizing Credit Card Fraud // Research result. Information technologies. – Т.10, №4, 2025. – P. 63-71. DOI: 10.18413/2518-1092-2025-10-4-0-5
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1. Benchaji I., Douzi S., El Ouahidi B. Credit card fraud detection Model Based on LSTM recurrent neural networks. Journal of Advances in Information Technology. Vol. 12. no. 2. 2021. Pp. 113–118. DOI: 10.12720/jait.12.2.113-118
2. Credit Card Fraud Detection: 2025 Trends and Interventions, FICO, 2025.
3. Ali M.A., Azad M.A., Centeno M.P., Hao F., van Moorsel A. Consumer-facing technology fraud: Economics, attack methods and potential solutions. Future Generation Computer Systems, 100, 2019. Pp. 408-427.
4. Budhram T. Lost, stolen or skimmed: Overcoming credit card fraud in South Africa. South African Crime Quarterly, 40, 2012. Pp. 31-37.
5. "Credit card fraud detection and risk management strategies" (2024/2025).
6. "Credit Card Fraud Data Analysis and Prediction Using Machine Learning" (2024/2025).
7. Kasongo S.M. An advanced intrusion detection system for IIoT based on GA and tree based algorithms. IEEE Access. 2021; 9: 113199–113212
8. Khatri S., Arora A., Agrawal A.P. Supervised machine learning algorithms for credit card fraud detection: a comparison. In: 10th international conference on cloud computing, data science & engineering (Confluence); 2020. p. 680-683.
9. Serzhan Y. Fraud Detection in Credit Card Transactions using Machine Learning: A Comparative Analysis. 2025.
10. Sundaravadivel P. et al. Optimizing credit card fraud detection with random forests and deep learning techniques. 2025.
11. Ghiasi M.M., Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput in Biology and Medicine. 2021; 128: 104089.
12. Lingjun H., Levine R.A., Fan J., Beemer J., Stronach J. Random forest as a predictive analytics alternative to regression in institutional research. Pract Assess Res Eval. 2020; 23(1): 1
13. Robles-Velasco A., Cortés P., Muñuzuri J., Onieva L. Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng Syst Saf. 2020; 196: 106754.
14. Seera M., Lim C.P., Kumar A., Dhamotharan L., Tan K.H. An intelligent payment card fraud detection system. Ann Oper Res 2021; 1–23
15. Hemavathi D., Srimathi H. Effective feature selection technique in an integrated environment using enhanced principal component analysis. J Ambient Intell Hum Comput. 2021; 12(3): 3679–3688.
16. Saheed Y.K., Hambali M.A., Arowolo M.O., Olasupo Y.A. Application of GA feature selection on Naive Bayes, random forest and SVM for credit card fraud detection. In: 2020 international conference on decision aid sciences and application (DASA); 2020. p. 1091–1097
17. Li Y., Jia M., Han X., Bai X.S. Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA). Energy. 2021; 225: 120331.
18. Ahmed K.H. A credit card fraud detection approach based on ensemble learning. 2025.
19. Trippi R.T., Turban E. (eds), Neural Networks in Finance and Investing, Probus Publishing Company. 1993.
20. Bhuiyan M. "Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Approaches" (2024).
21. Mienye I.D., Sun Y. Improved heart disease prediction using particle swarm optimization based stacked sparse autoencoder. Electronics. 2021;10(19):2347