A PSO-ACO based ANN Approach for Credit Card Fraud Detection
DOI:
https://doi.org/10.22399/ijcesen.981Keywords:
ACO, PSO, ANN, Fraud Detection, Computational EfficiencyAbstract
Credit card fraud is a major problem for both consumers and institutions in the ever-changing financial environment of today. Credit card fraud detection is a significant challenge in financial security, and a novel approach is proposed to enhance its accuracy. This work intends to detect fraudulent transactions on credit cards by use of PSO along with ACO in combination. The Hybrid PSO-ACO based ANN model uses PSO and ACO to refine the training process, resulting in improved classification performance. PSO optimizes the network's epoch settings and batch processing, while ACO fine-tunes batch selections. Experiments on two credit card transaction datasets show that the Hybrid PSO-ACO based ANN outperforms conventional ANN models and other optimization-based ANN approaches in terms of accuracy, sensitivity and specificity. The proposed model improves generalizing to fresh data, reduces overfitting, and balances minority class data. This work highlights the potential of combining multiple optimization techniques to advance fraud detection capabilities and provides a robust framework for future research
References
El Hlouli, F. Z., Riffi, J., Mahraz, M. A., Yahyaouy, A., El Fazazy, K., & Tairi, H. (2024). Weighted binary ELM optimized by the reptile search algorithm, application to credit card fraud detection. Multimedia Tools and Applications. 83(39);86383–86404. https://doi.org/10.1007/s11042-024-19508-x
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016). Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. IEEE Access. 4, 7940–7957. https://doi.org/10.1109/access.2016.2619719
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems. 29(8);3784–3797. https://doi.org/10.1109/tnnls.2017.2736643
Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2009). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion. 10(4);354–363. https://doi.org/10.1016/j.inffus.2008.04.001
Rufai, K. I., Usman, O. L., Muniyandi, R. C., & Oyinkanola, L. O. (2021). Modelling Credit Card Payment Fraud Detection System For Financial Institutions In Nigeria Using An Improved Firefly Algorithm. International Journal of Information Processing and Communication 11(1);9–25.
Manokaran, J., Vairavel, G., & Vijaya, J. (2023, September 15). A novel set theory rule-based hybrid feature selection technique for efficient anomaly detection system in IoT edge. In 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS). 1–6. https://doi.org/10.1109/iq-cchess56596.2023.10391717
Jena, J. J., Pandey, M., Rautaray, S. S., & Jena, S. (2021). Evolutionary algorithms-based machine learning models. In Trends of Data Science and Applications: Theory and Practices. 954;91–111. https://doi.org/10.1007/978-981-33-6815-6_5
Rauf, H. T., Shoaib, U., Lali, M. I., Alhaisoni, M., Irfan, M. N., & Khan, M. A. (2020). Particle swarm optimization with probability sequence for global optimization. IEEE Access. 8;110535–110549. https://doi.org/10.1109/access.2020.3002725
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18;30–55.
Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems. 50(3);559–569. https://doi.org/10.1007/s10618-008-0116-z
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review. 34(4), 321–344. https://doi.org/10.48550/arXiv.1009.6119
Kocyigit, E., Korkmaz, M., Sahingoz, O. K., & Diri, B. (2024, July 12). Enhanced feature selection using genetic algorithm for machine-learning-based phishing URL detection. Applied Sciences. 14(14), 6081. https://doi.org/10.3390/app14146081
Saheed, Y. K., Salau-Ibrahim, T. T., Abdulsalam, M., Adeniji, I. A., & Balogun, B. F. (2024, August 1). Modified bi-directional long short-term memory and hyperparameter tuning of supervised machine learning models for cardiovascular heart disease prediction in mobile cloud environment. Biomedical Signal Processing and Control. 94, 106319. https://doi.org/10.1016/j.bspc.2024.106319
Chalabi, N. E., Attia, A., Bouziane, A., Hassaballah, M., & Akhtar, Z. (2022, September 4). Recent trends in face recognition using metaheuristic optimization. In Handbook of Nature-Inspired Optimization Algorithms: The State of the Art. 85–112. https://doi.org/10.1007/978-3-031-07516-2_5
Rao, S., Verma, A. K., & Bhatia, T. (2021, December 30). A review on social spam detection: Challenges, open issues, and future directions. Expert Systems with Applications. 186, 115742. https://doi.org/10.1016/j.eswa.2021.115742
Nguyen, T. T., Tahir, H., Abdelrazek, M., & Babar, A. (2020). Deep learning methods for credit card fraud detection. arXiv preprint. https://doi.org/10.48550/arXiv.2012.03754
Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., & Kuruwitaarachchi, N. (2019, January 10). Real-time credit card fraud detection using machine learning. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 488–493.
Mienye, I. D., & Jere, N. (2024, July 11). Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions. IEEE Access.
Asha, R. B., & KR, S. K. (2021). Credit card fraud detection using artificial neural networks. Global Transitions Proceedings. 2(1);35–41. https://doi.org/10.1109/access.2024.3426955
Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by ANN and logistic regression. In Proceedings of the 2011 International Symposium on Innovations in Intelligent Systems and Applications. 315–319. https://doi.org/10.1109/inista.2011.5946108
Arora, S., & Kumar, D. (2017). Hybridization of SOM and PSO for detecting fraud in credit card. International Journal of Information Systems in the Service Sector. 9(3);17–36. https://doi.org/10.4018/ijisss.2017070102
Prusti, D., Rout, J. K., & Rath, S. K. (2023). Detection of credit card fraud by applying genetic algorithm and particle swarm optimization. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021. Springer Nature Singapore. 357–369. https://doi.org/10.1007/978-981-19-5868-7_27
Ghodsi, M., & Saniee Abadeh, M. (2017). Fraud detection of credit cards using neuro-fuzzy approach based on TLBO and PSO algorithms. Journal of Computer & Robotics. 10(2);57–68.
Yılmaz, A. A. (2023). A machine learning-based framework using the particle swarm optimization algorithm for credit card fraud detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering. 66(1);82–94. https://doi.org/10.33769/aupse.1361266
Singh, A., Jain, A., & Biable, S. E. (2022). Financial fraud detection approach based on firefly optimization algorithm and support vector machine. Applied Computational Intelligence and Soft Computing. 2022(1), 1468015. https://doi.org/10.1155/2022/1468015
Kamaruddin, A. S., Hadrawi, M. F., Wah, Y. B., & Aliman, S. (2023). An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction. Indonesian Journal of Electrical Engineering and Computer Science. 32(1);458–467. https://doi.org/10.11591/ijeecs.v32.i1.pp468-477
Btoush, E. A. L. M., Zhou, X., Gururajan, R., Chan, K. C., Genrich, R., & Sankaran, P. (2023). A systematic review of literature on credit card cyber fraud detection using machine and deep learning. PeerJ Computer Science. 9, e1278. https://doi.org/10.7717/peerj-cs.1278
Guo, Y., He, J., Xu, L., & Liu, W. (2019). A novel multi-objective particle swarm optimization for comprehensible credit scoring. Soft Computing. 23;9009–9023. https://doi.org/10.1007/s00500-018-3509-y
Jimbo Santana, P., Lanzarini, L., & Bariviera, A. F. (2019). Variations of particle swarm optimization for obtaining classification rules applied to credit risk in financial institutions of Ecuador. Risks. 8(1);2. https://doi.org/10.3390/risks8010002
Emambocus, B. A. S., Jasser, M. B., & Amphawan, A. (2023). A survey on the optimization of artificial neural networks using swarm intelligence algorithms. IEEE Access. 11, 1280–1294. https://doi.org/10.1109/access.2022.3233596
Waffa Abdul-Abbas Shehab, Haiffa Muhsan B. Alrikabi, Abeer A. Abdul–Razaq, Huda Karem Nasser, & Asaad Shakir Hameed. (2024). Comparative Analysis of New Solutions for the Capacitated Vehicle Routing Problem Against CVRPLIB Benchmark. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.626
I. Bhuvaneshwarri, M. Maheswari, C. Kalaivanan, P. Deepthi, Tatiraju V. Rajani Kanth, & V. Saravanan. (2025). Hybrid Swarm Intelligence-Based Neural Framework for Optimizing Real-Time Computational Models in Engineering Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1001
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