Fine-Tuned LSTM for Credit Card Fraud Detection and classification
Keywords:
SVM, XGBoost, Bagging, Neural Networks, LSTM.Abstract
Credit card fraud detection is crucial in the financial industry, where timely and accurate identification of fraudulent activities can prevent substantial financial losses. This study examines the efficacy of various machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), Bagging (Random Forest), Boosting (XGBoost), Neural Networks, and Long Short-Term Memory (LSTM) networks—in detecting credit card fraud. Rigorous testing revealed that the LSTM model achieved superior performance with an accuracy of 99%, surpassing all other evaluated models. This paper presents a comparative analysis of these algorithms, emphasizing the effectiveness of the fine-tuned LSTM model in identifying fraudulent transactions. The findings suggest that implementing LSTM can significantly enhance security measures in financial institutions.
Downloads
References
Bhattacharyya, D., & Jha, M. (2020). Credit Card Fraud Detection: A Machine Learning Approach.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., & Bontempi, G. (2015). Learned lessons in credit card fraud detection from a practitioner perspective.
He, X., He, Q., Bai, Y., & Garcia-Molina, H. (2017). Fraud Detection for Online Social Networks: A Deep Learning Approach.
Kiani, N. A., & Rahmani, A. M. (2020). Credit Card Fraud Detection Using Machine Learning Techniques.
Phua, C., Lee, V. C., Smith-Miles, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research.
Du, Z., Liu, C., Zhang, Y., & Xu, G. (2017). Credit Card Fraud Detection Based on Random Forest and SMOTE.
Credit card fraud detection: A realistic modeling and a novel learning strategy", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784-3797, August 2022.
M. Azhan, M. Ahmad and M. S. Jafri, "Metoo: Sentiment analysis using neural networks (grand challenge)", 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pp. 476-480, 2020.
Alex Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network", Physica D: Nonlinear Phenomena, vol. 404, pp. 132306, March 2020.
Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi, "Calibrating probability with undersampling for unbalanced classification", 2022 IEEE Symposium Series on Computational Intelligence, December 2022.
S P Maniraj, Aditya Saini, Shadab Ahmed and Swarna Deep Sarkar, "Credit card fraud detection using machine learning and data science", International Journal of Engineering Research and, vol. 08, no. 09, September 2019.
Saad, M. D. (2019). An intelligent credit card fraud detection approach based on semantic fusion of two classifiers. Springer-Verlag GmbH Germany.
Seunghye, L.,Jingwan, H., Mehriniso, Z., Hyeonjoon, M., & Jaehong, L. (2017). Background Information of Deep Learning for Structural Engineering. CIMNE.
Shen, A., Tong, R., & Deng, Y. (2007). Application of Classification Models on Credit Card Fraud Detection. Graduate University of the Chinese Academy of Sciences.
Shukur, H., & Kurnaz, S. (2019). Credit Card Fraud Detection using Machine Learning Methodology. IJCSMC, 8(3), 257–260.
Siddhartha, B., Sanjeev, J., Kurian, T., & Christopher, J. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, (50), 602–613. Srivastava, H. (2017). What is k-fold cross validation. Available from: https://magoosh.com/data-science/k-foldcross-validation/
Turban, E., King, D., McKay, J., Marshall, P., Lee, J., & Viehland, D. (2008). Electronic Commerce 2008: A Managerial Perspective. Pearson Education.
Victor,C., Le Minh Thao, D., Alessandro, D., & Zhili, S.(2022). Digital paymentfraud detection methodsin digital ages and Industry 4.0.Computers & Electrical Engineering, 100, 107734. doi:10.1016/j.compeleceng.2022.107734
Victor, C., Lewis, G., Paolo, M., Qianwen, A., Le Minh Thao, D., Karl, H., Sreeja, B., & Anna, K. (2022). A Survey on Intrusion Detection Systemsfor Fog and Cloud Computing. Future Internet, 14(3), 89. doi:10.3390/fi14030089
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
