Optimization of Credit Card Fraud Based on Roulette and Tournament Selection by Genetic Algorithm and Artificial Bee Colony

Öz

Credit card fraud has become a major problem, especially with the spread of e-commerce and the increase in online shopping during the COVID-19 period. Theft of credit card information and transactions made with cards belonging to others lead to legal and economic problems. The aim of this study is to develop Genetic Algorithm (GA) and Artificial Bee Colony (ABC) methods based on roulette and tournament selection instead of random selection in the determination and classification of these illegal credit card fraud transactions. A data set consisting of 28 attributes and 284,807 credit card transactions was used in the study. Fraud in credit card transactions was estimated using genetic algorithm and artificial bee colony and the obtained results were compared. Optimization methods such as GA and ABC developed based on roulette and tournament selection were analyzed separately according to linear, quadratic and exponential functions. Using the linear, quadratic and exponential functions for the test data, the GA based on roulette selection showed success rates of 98.6%, 98.46% and 98.6% in identifying credit card fraudulent transactions, respectively; With the GA based on tournament selection, it shows 98.53%, 98.33% and 98.66% success. In addition, for the test data, using linear, quadratic and exponential functions, ABC method achieved 98.6%, 97.86% and 97.93% success in determining credit card fraudulent transactions, respectively. Success results were calculated with different evaluation criteria such as accuracy, recall and F1-Score and performance evaluation was presented for each proposed method.

Anahtar Kelimeler

• artificial bee colony
• genetic algorithm
• linear
• roulette
• tournament.

Kaynakça

1. [1] Aytekin, A., & Yücel, Y. B. (2017). Yeni Ödeme Teknolojilerinin Iş Hayatina Etkileri. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 4(12), 11-33.
2. [2] Sikkandar, H., Saroja, S., Suseandhiran, N., & Manikandan, B. (2023). An intelligent approach for anomaly detection in credit card data using bat optimization algorithm. Inteligencia Artificial, 26(72), 202-222.
3. [3] Gadi, M. F. A., Wang, X., & do Lago, A. P. (2008, December). Comparison with parametric optimization in credit card fraud detection. In 2008 Seventh International Conference on Machine Learning and Applications (pp. 279-285). IEEE.
4. [4] Bakır, Ç., Temurtaş, H., & Yeşilyurt, F. (2023, September). Detection of Credit Card Fraud with Artificial Neural Networks. In Proceedings of the International Conference on Advanced Technologies (Vol. 11, pp. 38-43.
5. [5] Çılburunoğlu, K. (2023). Kredi Kartı Dolandırıcılık Tespitinde Makine Öğrenme Algoritmalarının Karşılaştırmalı Analizi. İstanbul Gedik Üniversitesi. Yüksek Lisans Tezi.
6. [6] Hussein A. (2023). Developing an Artificial Inteligence Based Aystem to Detect Fraud in Credit Card Transactions. Altınbaş Üniversitesi. Yüksek Lisans Tezi.
7. [7] Jovanovic, D., Antonijevic, M., Stankovic, M., Zivkovic, M., Tanaskovic, M., & Bacanin, N. (2022). Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics, 10(13), 2272.
8. [8] Akyüz, B. (2022). Topluluk Öğrenimi ile Kredi Kartı Dolandırıcılık Tespiti: Karşılaştırmalı Bir Analiz. Yıldız Teknik Üniversitesi. Yüksek Lisans Tezi.

9. [9] Shah, H. B. (2020). Comparing Machine Learning Algorithms For Credit Card Fraud Detection.
10. [10] Soylu, K. (2018). Kredi kartı sahte işlem tespiti.
11. [11] Save, P., Tiwarekar, P., Jain, K. N., & Mahyavanshi, N. (2017). A novel idea for credit card fraud detection using decision tree. International Journal of Computer Applications, 161(13).
12. [12] Shakya, R. (2018). Application of machine learning techniques in credit card fraud detection (Doctoral dissertation, University of Nevada, Las Vegas).
13. [13] Taha, A. A., & Malebary, S. J. (2020). An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access, 8, 25579-25587.
14. [14] Prabhakaran, N., & Nedunchelian, R. (2023). Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection. Computational Intelligence and Neuroscience, 2023.
15. [15] Prusti, D., Rout, J. K., & Rath, S. K. (2023, January). Detection of credit card fraud by applying genetic algorithm and particle swarm optimization. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021 (pp. 357-369). Singapore: Springer Nature Singapore.
16. [16] Tayebi, M., & El Kafhali, S. (2021, May). Hyperparameter optimization using genetic algorithms to detect frauds transactions. In The International Conference on Artificial Intelligence and Computer Vision (pp. 288-297). Cham: Springer International Publishing.
17. [17] Prusti, D., Rout, J. K., & Rath, S. K. (2023, January). Detection of credit card fraud by applying genetic algorithm and particle swarm optimization. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021 (pp. 357-369). Singapore: Springer Nature Singapore.
18. [18] Patidar, R., & Sharma, L. (2011). Credit card fraud detection using neural network. International Journal of Soft Computing and Engineering (IJSCE), 1(32-38).
19. [19] Vats, S. A. T. V. I. K., Dubey, S. K., & Pandey, N. K. (2013, July). Genetic algorithms for credit card fraud detection. In International Conference on Education and Educational Technologies.
20. [20] Geetha, N., & Dheepa, G. (2022). Transaction fraud detection using Artificial Bee Colony (ABC) based feature selection and Enhanced Neural Network (ENN) classifier. International Journal of Mechanical Engineering, 7(3).
21. [21] Karthikeyan, T., Govindarajan, M., & Vijayakumar, V. (2023). Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network. Intelligent Automation & Soft Computing, 37(2).
22. [22] Furlanetto, G. C., Gomes, V. Z., & Breve, F. A. (2023, June). Artificial Bee Colony Algorithm for Feature Selection in Fraud Detection Process. In International Conference on Computational Science and Its Applications (pp. 535-549). Cham: Springer Nature Switzerland.
23. [23] Küçüksille, E., & Tokmak, M. (2011). Yapay arı kolonisi algoritması kullanarak otomatik ders çizelgeleme. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 15(3), 203-210.
24. [24] Huang, H., vd. (2024). Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique. Applied Soft Computing, 154 (2024), 1-11.
25. [25] Duan, Y., vd. (2024). CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks. arXiv preprint arXiv:2402.14708, 1-10.
26. [26] Raphael, B. A., vd. (2023). Card fraud detection using artificial neural network and multilayer perception algorithm. International Journal of Algorithms Design and Analysis Review, 1(1), 21-30.
27. [27] Karthika, J., & Senthilselvi, A. (2023). Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique. Multimedia Tools and Applications, 82(20), 31691-31708.
28. [28] Khidmat, W. B., vd. (2023). Machine learning in the boardroom: gender diversity prediction using boosting and undersampling methods. Research in International Business and Finance, 66(2023), 1-17.
29. [29] Prusti, D., vd. (2023, January). Detection of credit card fraud by applying genetic algorithm and particle swarm optimization. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, Singapore, 357-369.
30. [30] Karthikeyan, vd. (2023). Intelligent financial fraud detection using artificial bee colony optimization based recurrent neural network. Intelligent Automation & Soft Computing, 37(2), 1485, 1498.