International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Effects of Feature Selection with Machine Learning Algorithms in Detection of Credit Card Fraud

Author : Surbhi Bhardwaj ¹ Sonika Gupta ²

Date of Publication :19th July 2022

Abstract: As an effect of developments in e-commerce systems and communication technologies, Credit cards have become the most common mode of payment for purchases. The payments through the credit cards also involve the risk of credit card fraud such as application fraud, identity theft, lost/stolen card misuse, and phishing. These frauds lead to huge losses and require automatic and real-time fraud detection. Many studies have used Machine Learning (ML) techniques to detect fraudulent transactions. This study focuses on proposing a framework for the detection of credit card frauds by applying machine learning techniques like Random Forest (RF) and NaÃ¯ve Bayes and testing the results on balanced and unbalanced datasets with and without performing the feature selection on the dataset. After comparing the results, it was discovered that Random Forest outperformed the NaÃ¯ve Bayes on a balanced dataset with feature selection performed using Recursive Feature Elimination and Information Gain.

Reference :

1. Forough, J., & Momtazi, S. (2021). Ensemble of deep sequential models for credit card fraud detection. Applied Soft Computing, 99, 106883.
2. Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences, 557, 302-316.
3. 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.
4. Rtayli, N., & Enneya, N. (2020). Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. Journal of Information Security and Applications, 55, 102596
5. Lucas, Y., Portier, P. E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393-402.
6. Saheed, Y. K., Hambali, M. A., Arowolo, M. O., & Olasupo, Y. A. (2020, November). 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) (pp. 1091-1097). IEEE
7. Li, Z., Liu, G., & Jiang, C. (2020). Deep representation learning with full center loss for credit card fraud detection. IEEE Transactions on Computational Social Systems, 7(2), 569-579
8. Kumari, P., & Mishra, S. P. (2019). Analysis of credit card fraud detection using fusion classifiers. In Computational Intelligence in Data Mining (pp. 111-122). Springer, Singapore.
9. Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455.
10. Pumsirirat, A., & Yan, L. (2018). Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. International Journal of advanced computer science and applications, 9(1), 18-25
11. Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284.
12. Carcillo, F., Le Borgne, Y. A., Caelen, O., & Bontempi, G. (2018). Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization. International Journal of Data Science and Analytics, 5(4), 285-300.
13. Nami, S., & Shajari, M. (2018). Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors. Expert Systems with Applications, 110, 381-392.
14. Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018, March). Random forest for credit card fraud detection. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-6). IEEE.
15. Fu, K., Cheng, D., Tu, Y., & Zhang, L. (2016, October). Credit card fraud detection using convolutional neural networks. In International conference on neural information processing (pp. 483-490). Springer, Cham.
16. Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142.
17. Business Today the business magazine India (Dec 15, 2021) available https://www.businesstoday.in/union-budget-2022/ banking/story/india-saw-229-banking-frauds-per-day-in-fy21 -less-than-1-amount-recovered-315685-2021-12-15.
18. Lakshmi, S. V. S. S., & Kavilla, S. D. (2018). Machine learning for credit card fraud detection system. International Journal of Applied Engineering Research, 13(24), 16819-16824.