A machine learning-based framework using the particle swarm optimization algorithm for credit card fraud detection

Year 2024, Volume: 66 Issue: 1, 82 - 94, 14.06.2024

Abdullah Asım Yılmaz

https://doi.org/10.33769/aupse.1361266

Abstract

The detection of fraudulent activities in credit cards transactions presents a significant challenge due to the constantly changing and unpredictable tactics used by fraudsters, who take advantage of technological advancements to evade security measures and cause substantial financial harm. In this paper, we suggested a machine learning based methodology to detect fraud in credit cards. The suggested method contains four key phases, including data normalization, data preprocessing, feature selection, classification. For classification artificial neural network, decision tree, logistic regression, naive bayes, random forest while for feature selection particle swarm optimization is employed. With the use of a dataset created from European cardholders, the suggested method was tested. The experimental results show that the suggested method beats the other machine learning techniques and can successfully classify frauds with a high detection rate.

Keywords

Credit card fraud detection, machine learning, particle swarm optimization

References

• Raghavan, P., El Gayar, N., Fraud detection using machine learning and deep learning, Int. Conf. on Comput. Intelligence and Knowledge Economy (ICCIKE), (2019), 334-339, https://doi.org/10.1109/ICCIKE47802.2019.9004231.
• Sisodia, D. S., Reddy, N. K., Bhandari, S., Performance evaluation of class balancing techniques for credit card fraud detection, IEEE Int. Conf. on Power, Control, Signals and Instrumentation Engineering (ICPCSI), (2017), 2747-2752, https://doi.org/10.1109/ICPCSI.2017.8392219.
• WorldPay, Global payments report preview: The guide to the world of online payments, (2015). Available at: http://offers.worldpayglobal.com/rs/850-JOA856/images/Global PaymentsReportNov2015.pdf. [Accessed August 2023].
• Federal Trade Commission, Consumer sentinel network - data book for January, (2022). Available at https://www.ftc.gov/. [Accessed August 2023].
• Bhatla, T. P., Prabhu, V., Dua, A., Understanding credit card frauds, Cards Business Rev., 6 (2003), 1-15.
• Sahin, Y., Duman, E., Detecting credit card fraud by decision trees and support vector machines, Int. MultiConf. of Engineers and Computer Scientists (IMECS), (2011), 1-5.
• Elkan, C., Magical thinking in data mining: Lessons from COIL challenge 2000, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, (2001), 426-431, https://doi.org/10.1145/502512.502576.
• Yilmaz, A. A., Guzel, M. S., Bostanci, E., Askerzade, I., A novel action recognition framework based on deep-learning and genetic algorithms. IEEE Access, 8 (2020), 100631-100644, https://doi.org/10.1109/ACCESS.2020.2997962.
• Aslan, Ö., Yilmaz, A. A., A new malware classification framework based on deep learning algorithms, IEEE Access, 8 (2021), 87936-87951, https://doi.org/10.1109/ACCESS.2021.3089586.
• Yilmaz, A. A., Guzel, M. S., Bostanci, E., Askerzade, I., A vehicle detection approach using deep learning methodologies, Int. Conf. on Theoretical and Applied Computer Science and Engineering (ICTACSE), (2018), 64-71.
• Yilmaz, A. A., A novel hyperparameter optimization aided hand gesture recognition framework based on deep learning algorithms, Trait. Du Signal, 39 (3) (2022), 823-833, https://doi.org/10.18280/ts.390307.
• Yilmaz, A. A., Intrusion detection in computer networks using optimized machine learning algorithms, Int. Informatics and Software Engineering Conf. (IISEC), (2022), 1-5, https://doi.org/10.1109/IISEC56263.2022.9998258.
• Yee, O. S., Sagadevan, S., Ahamed Hassain Malim, N. H., Credit card fraud detection using machine learning as data mining technique, JTEC, 10 (1-4) (2018), 23-27.
• Awoyemi, J. O., Adetunmbi, A. O., Oluwadare, S. A., Credit card fraud detection using machine learning techniques: A comparative analysis, Int. Conf. on Computer Networks and Information (ICCNI), (2017), 1-9, https://doi.org/10.1109/ICCNI.2017.8123782.
• Pumsirirat, A., Liu, Y, Credit card fraud detection using deep learning based on autoencoder and restricted Boltzmann machine, IJACSA, 9 (1) (2018), 18-25, https://doi.org/10.14569/IJACSA.2018.090103.
• Sahin, Y., Bulkan, S., Duman, E., A cost-sensitive decision tree approach for fraud detection, Expert Syst. Appl., 40 (15) (2013), 5916-5923, https://doi.org/10.1016/j.eswa.2013.05.021.
• Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., Anderla, A., Credit card fraud detection-machine learning methods, Int. Sympos. INFOTEH-JAHORINA, (2019), 1-5, https://doi.org/10.1109/INFOTEH.2019.8717766.
• Kaggle Datasets: The credit card fraud detection dataset, (2013). Available at: https://www.kaggle.com/mlg-ulb/creditcardfraud. [Accessed August 2023].
• Guo, S., Liu, Y., Chen, R., Sun, X., Wang, X. X., Improved SMOTE algorithm to deal with imbalanced activity classes in smart homes, Neural Process. Lett., 50 (2) (2019) , 1503-1526, https://doi.org/10.1007/s11063-018-9940-3.
• Jain, A., Nandakumar, K., Ross, A., Score normalization in multimodal biometric systems, Pattern Recognit., 38 (12) (2005), 2270-2285, https://doi.org/10.1016/j.patcog.2005.01.012.
• Khan, S. U., Yang, S., Wang, L., L. Liu., A modified particle swarm optimization algorithm for global optimizations of inverse problems, IEEE Trans. Magn., 52 (3) (2016), 1-4, https://doi.org/10.1109/TMAG.2015.2487678.
• Selvi, V., Umarani, R., Comparative analysis of ant colony and particle swarm optimization techniques, IJCA, 5 (4) (2010), 1-6, https://doi.org/10.5120/908-1286.
• Shi, Y., Eberhart, R. C., Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), (1999), 1945-1950, https://doi.org/10.1109/CEC.1999.785511.
• Krohling, R. A., Gaussian swarm: A novel particle swarm optimization algorithm, IEEE Conf. on Cybernetics and Intelligent Systems, (2004), 372-376, https://doi.org/10.1109/ICCIS.2004.1460443.
• Bai, Q., Analysis of particle swarm optimization algorithm, Comput. Inf. Sci., 3 (1) (2010), 180-184, https://doi.org/10.5539/cis.v3n1p180.
• Tyagi, A., Sharma, N., Sentiment Analysis using logistic regression and effective word score heuristic, IJET, 7 (2) (2018), 20-23, https://doi.org/10.14419/ijet.v7i2.24.11991.
• Kaur, H., Mangat V., A survey of sentiment analysis techniques, Int. Conf. on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), (2017), 921-925, https://doi.org/10.1109/I-SMAC.2017.8058315.
• Mamtesh, M., Mehla, S., Sentiment analysis of movie reviews using machine learning classifiers, IJCA, 182 (50) (2019), 25-28, https://doi.org/10.5120/ijca2019918756.
• Hemmatian, F., Sohrabi, M. K., A survey on classification techniques for opinion mining and sentiment analysis, Artif. Intell. Rev., 52 (3) (2019), 1495-1545, https://doi.org/10.1007/s10462-017-9599-6.
• Alsaeedi, A., Khan, M. Z., A study on sentiment analysis techniques of twitter data, IJACSA, 10 (2) (2019), 361-374, https://doi.org/10.14569/IJACSA.2019.0100248.
• AnalyticsVidhya: Important model evaluation error metrics, (2019). Available at: https://www.analyticsvidhya.com/blog/2019/08/11important-model-evaluation-error metrics. [Accessed August 2023].
• Khare, N., Sait, S. Y., Credit card fraud detection using machine learning models and collating machine learning models, IJPAM, 118 (20) (2018), 825-838.
• Seera, M., Lim, C. P., Kumar, A., Dhamotharan, L., Tan, K. H., An intelligent payment card fraud detection system, Ann. Oper. Res., 8 (2021), 1-23, https://doi.org/10.1007/s10479-021-04149-2.
• Dornadula, V. N., Geetha, S., Credit card fraud detection using machine learning algorithms, Procedia Comput. Sci., 165 (2019), 631-641, https://doi.org/10.1016/j.procs.2020.01.057.