Credit card fraud (CCF (is a serious problem that impacts a number of industries, including banking, e-commerce, insurance, finance, commercial entities, and private citizens. More sophisticated technologies are needed for effective detection as fraudulent activities become more complex. Machine learning (ML) approaches have demonstrated high performance in CCF identification. In this study, we explore six ML techniques: CatBoost, Hist Gradient Boosting, Decision Tree (DT), LightGBM (LGBM), ANN, and XGBoost (XGBC). To represent the data, identify its key characteristics, and compare their performance to determine the most successful fraud detection algorithm. Among these algorithms, the CatBoost algorithm, known for its high accuracy and superior performance, stands out. The proposed approach was based on the Kaggle dataset, and the results showed that CatBoost achieved the highest performance with a lower error rate compared to other models. This study also discusses the challenges related to implementing these advanced techniques, including the requirement for sizable, high-quality datasets and significant computational resources for model training and deployment. Key performance measures were used to evaluate the techniques' effectiveness, and CatBoost achieved a test accuracy of 0.9966, outperforming Hist Gradient Boosting, DT, LGBM, XGBC and ANN.
F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” IEEE Access, vol. 10, pp. 39700–39715, 2022, doi: 10.1109/ACCESS.2022.3166891.
F. M. Talaat, A. Aljadani, M. Badawy, and M. Elhosseini, “Toward interpretable credit scoring: Integrating explainable artificial intelligence with deep learning for credit card default prediction,” Neural Computing and Applications, vol. 36, no. 9, pp. 4847–4865, 2024, doi: 10.1007/s00521-023-09232-2.
G. Zioviris, K. Kolomvatsos, and G. Stamoulis, “An intelligent sequential fraud detection model based on deep learning,” The Journal of Supercomputing, vol. 80, no. 10, pp. 14824–14847, 2024, doi: 10.1007/s11227-024-06030-y.
J. M. Akinbusola Olushola, “Fraud detection using machine learning,” ScienceOpen, 2024, doi: 10.14293/PR2199.000647.v1.
M. Z. Mizher and A. B. Nassif, “Deep CNN approach for unbalanced credit card fraud detection data,” in Proc. 2023 Advances in Science and Engineering Technology Int. Conf. (ASET), Feb. 2023, pp. 1–7, doi: 10.1109/ASET56582.2023.10180615.
D. Sehrawat and Y. Singh, “Auto-encoder and LSTM-based credit card fraud detection,” SN Computer Science, vol. 4, no. 5, p. 557, Jul. 2023, doi: 10.1007/s42979-023-01977-w.
J. Forough and S. Momtazi, “Ensemble of deep sequential models for credit card fraud detection,” Applied Soft Computing, vol. 99, p. 106883, Feb. 2021, doi: 10.1016/j.asoc.2020.106883.
I. D. Mienye and N. Jere, “Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions,” IEEE Access, vol. 12, pp. 96893–96910, 2024, doi: 10.1109/ACCESS.2024.3426955.
T. Berhane, T. Melese, A. Walelign, and A. Mohammed, “A hybrid convolutional neural network and support vector machine-based credit card fraud detection model,” Mathematical Problems in Engineering, vol. 2023, Jan. 2023, doi: 10.1155/2023/8134627.
B. Baykara and A. Uk, “Impact of evaluation methods on decision tree accuracy,” 2015.
B. Charbuty and A. Abdulazeez, “Classification based on decision tree algorithm for machine learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 1, pp. 20–28, 2021, doi: 10.38094/jastt20165.
M. Ibrahim, H. AbdelRaouf, K. Amin, and N. Semary, “Keystroke dynamics based user authentication using histogram gradient boosting,” International Journal of Computers and Information, Sep. 2022, doi: 10.21608/ijci.2022.155605.1081.
M. Shehab, R. Taherdangkoo, and C. Butscher, “Towards reliable barrier systems: A constrained XGBoost model coupled with gray wolf optimization for maximum swelling pressure of bentonite,” Computers and Geotechnics, vol. 168, p. 106132, 2024, doi: 10.1016/j.compgeo.2024.106132.
T. Chen and C. Guestrin, “XGBoost,” in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2016, pp. 785–794, doi: 10.1145/2939672.2939785.
A. Ahmad, A. A. Ahmad, Y. Lasheng, M. Kholief, and A. Garba, “Evaluation of convolutional neural network and gradient boosting methods for bug severity classification,” Mar. 2024, doi: 10.13140/RG.2.2.26560.96002.
F. Alzamzami, M. Hoda, and A. El Saddik, “Light gradient boosting machine for general sentiment classification on short texts: A comparative evaluation,” IEEE Access, vol. 8, pp. 101840–101858, 2020, doi: 10.1109/ACCESS.2020.2997330.
V. N. Ogar, S. Hussain, and K. A. A. Gamage, “Transmission line fault classification of multi-dataset using CatBoost classifier,” Signals, vol. 3, no. 3, pp. 468–482, Jul. 2022, doi: 10.3390/signals3030027.
M. Habeeb, “Enhanced Android malware detection through artificial neural networks technique,” Mesopotamian Journal of CyberSecurity, vol. 5, 2025, doi: 10.58496/MJCS/2025/005.
Ž. Ð. Vujovic, “Classification model evaluation metrics,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, 2021, doi: 10.14569/IJACSA.2021.0120670.
W. P. Tanner and J. A. Swets, “A decision-making theory of visual detection,” Psychological Review, vol. 61, no. 6, pp. 401–409, 1954, doi: 10.1037/h0058700.
S. Li, High Performance Parallelism Pearls, 2015, doi: 10.1016/B978-0-12-803819-2.00013-6.
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