A Comprehensive Review of Advanced Algorithms for Detecting Plastic Money Fraud
Keywords:
CNN, Machine Learning, Big Data CNN, ANN, Random Forest, Naive BayesAbstract
The increasing reliance on plastic money, such as credit and debit cards, for online transactions has led to a rise in fraudulent activities by cybercriminals seeking to exploit financial systems. Detecting and mitigating these fraudulent transactions is crucial to ensuring financial security. Machine learning algorithms provide an effective approach for identifying suspicious transactions by analysing patterns within transactional datasets. In this study, we explore the use of machine learning techniques, including Convolutional Neural Networks (CNN,) Random Forest, Naïve Bayes, and Artificial Neural Networks (ANN), to classify transactions as legitimate or fraudulent. Additionally, we investigate the performance improvements achieved through the hybridization of two models to enhance accuracy and detection efficiency. The proposed approach enables financial institutions to proactively identify fraudulent activities and implement necessary preventive measures. The results demonstrate the effectiveness of machine learning in fraud detection, contributing to a more secure and reliable digital financial ecosystem.
Downloads
References
A. Prasad, R. Dev, G. Shobha and J. Shetty, "Machine Learning Techniques to Detect Fraud in Credit Cards on the HPCC Systems Platform," 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India, 2019, pp. 1-7, doi: 10.1109/CSITSS47250.2019.9031032.
Ileberi, E., Sun, Y. & Wang, Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data 9, 24 (2022). https://doi.org/10.1186/s40537-022-00573-8
I. D. Mienye and N. Jere, "Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions," in IEEE Access, vol. 12, pp. 96893-96910, 2024, doi: 10.1109/ACCESS.2024.3426955.
M. L. Gambo, A. Zainal and M. N. Kassim, "A Convolutional Neural Network Model for Credit Card Fraud Detection," 2022 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, 2022, pp. 198-202, doi: 10.1109/ICoDSA55874.2022.9862930.
Z. Zhang and S. Huang, "Credit Card Fraud Detection via Deep Learning Method Using Data Balance Tools," 2020 International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, China, 2020, pp. 133-137, doi: 10.1109/ICCSMT51754.2020.00033.
A. A. El Naby, E. El-Din Hemdan and A. El-Sayed, "Deep Learning Approach for Credit Card Fraud Detection," 2021 International Conference on Electronic Engineering (ICEEM), Menouf, Egypt, 2021, pp. 1-5, doi: 10.1109/ICEEM52022.2021.9480639.
Benchaji, I., Douzi, S., El Ouahidi, B. et al. Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J Big Data 8, 151 (2021). https://doi.org/10.1186/s40537-021-00541-8
J. Chen, Y. Shen and R. Ali, "Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network," 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2018, pp. 1054-1059, doi: 10.1109/IEMCON.2018.8614815.
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," in IEEE Access, vol. 10, pp. 39700-39715, 2022, doi: 10.1109/ACCESS.2022.3166891.
F. Alshameri and R. Xia, "An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection," in Big Data Mining and Analytics, vol. 7, no. 3, pp. 718-729, September 2024, doi: 10.26599/BDMA.2023.9020035.
Vanini, P., Rossi, S., Zvizdic, E. et al. Online payment fraud: from anomaly detection to risk management. Financ Innov 9, 66 (2023). https://doi.org/10.1186/s40854-023-00470-w
Leevy, J.L., Hancock, J. & Khoshgoftaar, T.M. Comparative analysis of binary and one-class classification techniques for credit card fraud data. J Big Data 10, 118 (2023). https://doi.org/10.1186/s40537-023-00794-5
Kennedy, R.K.L., Salekshahrezaee, Z., Villanustre, F. et al. Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J Big Data 10, 106 (2023). https://doi.org/10.1186/s40537-023-00750-3
Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
Swiderski, B., Osowski, S., Gwardys, G. et al. Random CNN structure: tool to increase generalization ability in deep learning. J Image Video Proc. 2022, 3 (2022). https://doi.org/10.1186/s13640-022-00580-y
K. J and A. Senthilselvi, "Detection of Credit Card Fraud Detection Using HPO with Inception Based Deep Learning Model," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 70-77, doi: 10.1109/ICIRCA57980.2023.10220771.
V. R. Adhegaonkar, A. R. Thakur and N. Varghese, "Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods," 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2024, pp. 792-796, doi: 10.1109/IDCIoT59759.2024.10467999.
H. Aldosari, "Garra Rufa Fish Optimization-based K-Nearest Neighbor for Credit Card Fraud Detection," 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India, 2024, pp. 1-5, doi: 10.1109/ICDCOT61034.2024.10516188.
B. Paulraj, "Machine Learning Approaches for Credit Card Fraud Detection: A Comparative Analysis and the Promise of 1D Convolutional Neural Networks," 2024 7th International Conference on Information and Computer Technologies (ICICT), Honolulu, HI, USA, 2024, pp. 82-92, doi: 10.1109/ICICT62343.2024.00020.
M. M. Khaled and Z. AL Aghbari, "ccfDetector: Utilizing GAN and Deep Learning for Credit Card Fraud Detection," 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2023, pp. 1-6, doi: 10.1109/ASET56582.2023.10180825.
M. Z. Mizher and A. B. Nassif, "Deep CNN approach for Unbalanced Credit Card Fraud Detection Data," 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2023, pp. 1-7, doi: 10.1109/ASET56582.2023.10180615.
M. N. Yousuf Ali, T. Kabir, N. L. Raka, S. Siddikha Toma, M. L. Rahman and J. Ferdaus, "SMOTE Based Credit Card Fraud Detection Using Convolutional Neural Network," 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2022, pp. 55-60, doi: 10.1109/ICCIT57492.2022.10054727.
M. Z. Mizher and A. B. Nassif, "Deep CNN approach for Unbalanced Credit Card Fraud Detection Data," 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2023, pp. 1-7, doi: 10.1109/ASET56582.2023.10180615.
S. M. Gopavaram and P. Vinothiyalakshmi, "Cloud Based Credit Card Fraud Detection System in Banking Using Machine Learning and Deep Learning algorithms," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-4, doi: 10.1109/ICCCNT56998.2023.10307070.
Kennedy, R.K.L., Villanustre, F., Khoshgoftaar, T.M. et al. Synthesizing class labels for highly imbalanced credit card fraud detection data. J Big Data 11, 38 (2024). https://doi.org/10.1186/s40537-024-00897-7.
