Comparative Analysis of K-Nearest Neighbors, Naive Bayes, and Decision Tree Algorithms for Credit Card Fraud Detection
Abstract
This study is based on the comparative analysis of K-Nearest Neighbors (KNN), Naive Bays, and Decision Trees based on the machine learning field and applied in the fraud detection of credit cards. The algorithms are evaluated and compared based on computational efficiency, classification metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results reveal that KNN has the highest overall accuracy, but it has low recall in the detection of fraudulent activity; Naive Bayes has the highest recall, but suffers from a high false positive rate and low accuracy; Decision Tree is the most stable algorithm and maintains a competitive accuracy of 95.3% with reasonable trade-offs for precision and recall. In credit card fraud detection, particularly for imbalanced datasets, this study recommends a mechanism for balancing two conflicting perspectives: computational weight and accuracy, with Decision Tree being the most effective among the three algorithms considered.
Keywords
Credit Card; Fraud Detection; Machine Learning; Algorithms; imbalance
References
Alhabib, A. A., Alasiri, A. F., Alharbi, M. B., Ahmad, S., & Eljialy, A. E. M. (2024). Credit card fraud detection using Random Forest and K-Nearest Neighbors (KNN) algorithms. In Proceedings of the 5th International Conference on Computing, Communication, and Cyber-Security (IC4S) (pp. 383–395). Springer. https://doi.org/10.1007/978-981-97- 7371-8_30 Kaul, A., Chhabra, M., Sachdeva, P., Jain, R., & Nagrath, P. (2021). Credit card fraud detection using different ML and DL techniques. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3747486 Zhang, Y., Li, X., & Liu, Y. (2023). Federated learning model for credit card fraud detection with data imbalance. Journal of Ambient Intelligence and Humanized Computing, 14, 1– https://doi.org/10.1007/s00521-023-09410-2 Abraham, N. (2024). Credit card fraud detection using machine learning and deep learning. GitHub. https://github.com/NissyAbrahamA/credit-card-fraud-detection Rzayeva, D., & Malekzadeh, S. (2022). A combination of deep neural networks and K-Nearest Neighbors for credit card fraud detection. arXiv Preprint, arXiv:2205.15300. https://doi.org/10.48550/arXiv.2205.15300 Talukder, M., Hossen, R., Uddin, M. A., Uddin, M. N., & Acharjee, U. K. (2024). Securing transactions: A hybrid dependable ensemble machine learning model using IHT-LR and grid search. arXiv Preprint, arXiv:2402.14389. https://doi.org/10.48550/arXiv.2402.14389 Zhu, M., Zhang, Y., Gong, Y., Xu, C., & Xiang, Y. (2024). Enhancing credit card fraud detection: A neural network and SMOTE integrated approach. arXiv preprint arXiv:2405.00026. https://doi.org/10.48550/arXiv.2405.00026 Verma, S., & Dhar, J. (2024). Credit card fraud detection: A deep learning approach. arXiv preprint arXiv:2409.13406. https://doi.org/10.48550/arXiv.2409.13406 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(8), 6231–6256. https://doi.org/10.1007/s00521-023-09410-2 Kim, J. S., & Park, T. W. (2023). A comparative study on credit card fraud detection using machine learning techniques. Journal of Finance and Data Science, 9(1), 115–128. https://doi.org/10.1016/j.jfds.2022.10.001 Khan, M. A., Naeem, M., & Iqbal, Z. (2023). A hybrid deep learning approach for credit card fraud detection using convolutional neural networks and decision trees. Expert Systems with Applications, 207, 118062. https://doi.org/10.1016/j.eswa.2023.118062 Liu, J., Wang, H., & Li, Q. (2023). Secure transaction systems using ensemble machine learning for credit card fraud detection. Future Generation Computer Systems, 148, 583– https://doi.org/10.1016/j.future.2023.08.011 Patel, A., Sharma, K., & Mishra, P. (2024). Random Forest and KNN-based hybrid model for enhanced credit card fraud detection. In Proceedings of the 6th International Conference on Advances in Computing and Data Sciences (pp. 150–162). Springer. https://doi.org/10.1007/978-981-99-7078-9_12 Cheng, H., Li, X., & Zhang, J. (2023). Federated learning for imbalanced credit card fraud detection using adaptive sampling techniques. Journal of Applied Artificial Intelligence, 45(5), 843–861. https://doi.org/10.1080/08839514.2023.1245123 Wong, T. K., & Chan, A. C. (2022). Comparative analysis of machine learning and deep learning models for credit card fraud detection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3964578 Roy, S., & Jain, S. (2023). An ensemble learning approach for real-time credit card fraud detection. Journal of Intelligent & Fuzzy Systems, 45(3), 2675–2685. https://doi.org/10.3233/JIFS-223461 Nguyen, T. D., Pham, H. Q., & Bui, N. T. (2022). Credit card fraud detection using LSTM and autoencoder-based deep learning models. Procedia Computer Science, 201, 1045–1052. https://doi.org/10.1016/j.procs.2022.04.135 Ahmed, K., Khan, M. S., & Rehman, S. U. (2023). Credit card fraud detection using XGBoost and deep neural networks: A hybrid model approach. Journal of Financial Crime, 30(2), 523–536. https://doi.org/10.1108/JFC-09-2022-0208 Sharma, A., & Patel, R. (2023). Comparative study of Naive Bayes and deep learning techniques for credit card fraud detection. Journal of Intelligent Systems, 32(4), 765–780. https://doi.org/10.1515/jisys-2023-0098 Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2021).. Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245. https://doi.org/10.1016/j.eswa.2021.112097 Lin, J., Liu, X., & Zhang, D. (2021). Research on the KNN algorithm for detecting fraudulent credit card transactions. Journal of Intelligent & Fuzzy Systems, 41(2), 2071–2081. Uddin, F., Woo, J., & Lee, B. (2022). Fraud detection using improved K-nearest neighbors classification. Applied Sciences, 12(3), 1187–1202. https://doi.org/10.3390/app12031187 Agarwal, R., & Agarwal, R. (2021). Naive Bayes for fraud detection in banking sector. International Journal of Computer Applications, 183(26), 31–35. Kamal, A., Sadeghi, A., & Zhou, Y. (2022). Real-time credit card fraud detection with Naive Bayes in big data streams. IEEE Access, 10, 55342–55351. https://doi.org/10.1109/ACCESS.2022.3178412 Ghosh, A., & Gupta, S. (2021). Decision tree-based intelligent model for fraud detection. Neural Computing and Applications, 33(4), 11213–11229. https://doi.org/10.1007/s00521-020-05603-w Hassan, A., Iddrisu, M. B. H., Adebayo, O., Oladipo, O., Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., Alonge, O. A., Alhassan, I., Ibrahim, M., Suleiman, M., Mohammed, A., Bello, M., Usman, A., Musa, A., Lawal, I., & Abdullahi, M. (2022). Hybrid machine learning approach for financial fraud detection using decision trees. IEEE Transactions on Computational Social Systems, 9(2), 456–465. https://doi.org/10.1109/TCSS.2021.3065478 Wang, L., & Zhang, Y. (2023). A probabilistic approach to credit card fraud detection using Gaussian Naive Bayes. Journal of Financial Data Science and Analytics, 6(2), 101–115. https://doi.org/10.1007/s42425-023-00189-2
