Journal of Industrial Engineering and Management Studies

Hyperparameter Optimization based on grid search to detect fraud transactions in the banking industry (SVM)

Document Type : Original Article

Authors

Hassan Farsijani 1
maryam asadi 2

1 Department of Management and Accounting, Shahid Beheshti University, Tehran, Iran

2 Faculty of Management, Department of Information Technology Management, Islamic Azad University, North Tehran Branch, Tehran, Iran

https://doi.org/10.22116/jiems.2025.448114.1552
Abstract
The ever-increasing volume and number of transactions in the bank make the fraud monitoring and detection process very complicated, costly, and time-consuming. In recent years, the development of new technologies has opened many ways for fraudsters and criminals to commit fraud. In this research, data mining methods are investigated in order to detect fraud in bank transactions. In order to detect fraud in bank card transactions, which are very unbalanced data types, the optimization of the support vector machine algorithm with hyperparameter techniques is presented and simulated on the Kaggle website data set, which includes bank card transactions, in the Python software environment has taken. The presented model benefits from bank transaction data and has the ability to extract complex patterns. As an effective optimization method, grid search technique intelligently adjusts the parameters of the support vector machine algorithm. The results of the model evaluation show that the support vector machine has a significant improvement in the detection of fraud patterns according to the criteria of accuracy and correctness. The combination of support vector machine and grid search technique as an innovative solution can help to improve the security of bank transactions in the digital age. In this research, hyperparameter optimization and smote balancing methods were used to reduce the number of false alarms. The proposed model can be commercialized and connected to the electronic banking system, online or offline, to detect fraudulent actions in transactions. The proposed model can be commercialized and connected to the electronic banking system, online or offline, to detect fraudulent actions in transaction.

Keywords

Optimization
Grid Search
Fraud
Support Vector Machine
safe Transaction
Benchaji, I., Douzi, S., & El Ouahidi, B. (2019). Using genetic algorithm to improve classification of imbalanced datasets for credit card fraud detection. In Smart Data and Computational Intelligence: Proceedings of the International Conference on Advanced Information Technology, Services and Systems (AIT2S-18) Held on October 17–18, 2018 in Mohammedia 3 (pp. 220-229). Springer International Publishing.
Burkov, A. (2019). The hundred-page machine learning book (Vol. 1, p. 32). Quebec City, QC, Canada: Andriy Burkov.
Chen, R. C., Luo, S. T., Liang, X., & Lee, V. C. (2005, October). Personalized approach based on SVM and ANN for detecting credit card fraud. In 2005 international conference on neural networks and brain (Vol. 2, pp. 810-815). IEEE.
Choi, D., & Lee, K. (2018). An artificial intelligence approach to financial fraud detection under IoT environment: A survey and implementation. Security and Communication Networks, 2018.
Gyamfi, N. K., & Abdulai, J. D. (2018, November). Bank fraud detection using support vector machine. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 37-41). IEEE.
Li, C., Ding, N., Zhai, Y., & Dong, H. (2021). Comparative study on credit card fraud detection based on different support vector machines. Intelligent Data Analysis, 25(1), 105-119.
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9(1), 1-17.
Mienye, I. D., & Sun, Y. (2023). A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection. IEEE Access, 11, 30628-30638.
Ni, L., Li, J., Xu, H., Wang, X., & Zhang, J. (2023). Fraud feature boosting mechanism and spiral oversampling balancing technique for credit card fraud detection. IEEE Transactions on Computational Social Systems.
Ojugo, A. A., & Nwankwo, O. (2021). Spectral-cluster solution for credit-card fraud detection using a genetic algorithm trained modular deep learning neural network. JINAV: Journal of Information and Visualization, 2(1), 15-24.
Patel, R. D., & Singh, D. K. (2013). Credit card fraud detection & prevention of fraud using genetic algorithm. International Journal of Soft Computing and Engineering, 2(6), 292-294.
Pouramirarsalani, A., Khalilian, M., & Nikravanshalmani, A. (2017). Fraud detection in E-banking by using the hybrid feature selection and evolutionary algorithms. International Journal of Computer Science and Network Security, 17(8), 271-279.
Rabbani, M., Abazari, A., & Farrokhi-Asl, H. (2020). An economic analysis for integrated bi-objective biofuel supply chain design using support vector machine. Journal of Industrial Engineering and Management Studies, 7(2), 77-97.
Sadgali, I., Sael, N., & Benabbou, F. (2018). Detection of credit card fraud: State of art. Int. J. Comput. Sci. Netw. Secur, 18(11), 76-83.
Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916-5923.
Seera, M., Lim, C. P., Kumar, A., Dhamotharan, L., & Tan, K. H. (2021). An intelligent payment card fraud detection system. Annals of operations research, 1-23.
Srokosz, M., Bobyk, A., Ksiezopolski, B., & Wydra, M. (2023). Machine-Learning-Based Scoring System for Antifraud CISIRTs in Banking Environment. Electronics, 12(1), 251.
Sudha, C., & Akila, D. (2021, January). Credit card fraud detection system based on operational & transaction features using svm and random forest classifiers. In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 133-138). IEEE.
Tiwari, P., Mehta, S., Sakhuja, N., Kumar, J., & Singh, A. K. (2021). Credit card fraud detection using machine learning: a study. arXiv preprint arXiv:2108.10005.
Voican, O. (2021). Credit Card Fraud Detection using Deep Learning Techniques. Informatica Economica, 25(1).
Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26-40.
West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66.
Journal of Industrial Engineering and Management Studies
Volume 11, Issue 2
December 2024
Pages 97-104

Home

Guide for Authors

Publication Ethics

Submit Manuscript

Reviewers

Contact Us