Modelling Credit Card Fraud Data using Machine Learning Algorithms
How to cite (IJASEIT) :
Credit card fraud refers to the unauthorized use of a credit card, often for illegitimate or illegal transactions. In recent years, it has emerged as a major concern, causing billions of dollars in losses annually, according to statistics. Moreover, the problem is becoming increasingly complex with the development of new fraud techniques. This alarming statistic underscores the urgent need for robust statistical analysis to understand, prevent, and combat fraudulent activities using credit card fraud data generated by European credit cardholders. Therefore, employing machine learning models with high accuracy ratings and optimal performance is essential for detecting credit card fraud. This study uses supervised machine learning techniques; decision trees (DT), Random Forests (RF), Artificial Neural Networks (ANN), Naïve Bayes (NB), and Logistic Regression (LR) to detect credit card fraud. The findings reveal that while identity theft, skimming, counterfeit cards, mail intercept fraud, and lost or stolen cards remain prevalent, there is a notable increase in other forms of fraud due to evolving techniques. Among the machine learning models evaluated, the Decision Tree method demonstrated the highest accuracy, outperforming the others.
Chuprina, R. (2021, February 25). Credit Card Fraud Detection: Top ML Solutions in 2021.
Lakshmi, M. G., Mettu, V., and Hameed, K. (2020). Credit Card Fraud Detection Using Random Forest. Journal of Information and Computational Science, 10(3). Retrieved from www/joics.org.
Shakya, R. (2018). Application of Machine Learning Techniques in Credit Card Fraud Detection. University of Nevada, Las Vegas, Department of Computer Science.
Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557(xxxx),317–331, doi:10.1016/j.ins.2019.05.042.
Chung J, Lee K. Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression. Sensors (Basel). 2023 Sep 10;23(18):7788. doi: 10.3390/s23187788. PMID: 37765845; PMCID: PMC10535547
Vaishnavi Nath Dornadula, S Geetha, Credit. Card Fraud Detection using Machine Learning Algorithms, Procedia Computer Science, Volume 165, 2019, Pages 631-641, ISSN 1877-0509, doi:10.1016/j.procs.2020.01.057.
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Gianluca Bontempi(2017 ). Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy, September 2017, IEEE Transactions on Neural Networks and Learning Systems PP(99):1-14. doi: 10.1109/TNNLS.2017.2736643.
Hosmer, D. W., and Lemeshow, S. (2000). Applied Logistic Regression (2nd ed.). New York: John Wiley and Sons., Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Thousand Oaks, CA: Sage Publications.
Bolton, R. J., and Hand, D. J. (2002). “Statistical fraud detection: A review.” Statistical Science, 17, 235-249.
Böhme, R., and Moore, T. (2016). “The economics of cybersecurity: Principles and policy options.” CESifo Economic Studies, 62
Bose, I., and Chen, X. (2016). “Detecting payment card fraud using recurrent neural networks.” Decision Support Systems, 87, 27-36.
Canova, G., Lui, J., and Qin, B. (2016). “Credit card fraud detection for imbalanced data using convolutional neural networks.” Expert Systems with Applications, 62, 90-102.
Emmanuel, I., Sun, Y. and Wang, Z. A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method. J Big Data 11, 23 (2024). doi:10.1186/s40537-024-00882-0.
Adepoju, O., Wosowei, J., lawte, S., & Jaiman, H. (2019). Comparative evaluation of credit card fraud detection using machine learning techniques. 2019 Global Conference for Advancement in Technology (GCAT). doi:10.1109/gcat47503.2019.8978372
Safa, M. U., & Ganga, R. M. (2019). Credit Card Fraud Detection Using Machine Learning. International Journal of Research in Engineering, Science and Management, 2(11).
Saheed, Y. K., Hambali, M. A., Arowolo, M. O., & Olasupo, Y. A. (2020). Application of ga feature selection on Naive Bayes, random forest and SVM for credit card fraud detection. 2020 International Conference on Decision Aid Sciences and Application (DASA). doi:10.1109/dasa51403.2020.9317228
Itoo, F., Meenakshi, & Singh, S. (2020). Comparison and analysis of logistic regression, Naïve Bayes and Knn Machine Learning Algorithms for credit card fraud detection. International Journal of Information Technology, 13(4), 1503–1511. doi:10.1007/s41870-020-00430-y.
Ogundunmade TP, Abidoye M, Olunfunbi OM. Modelling Residential Housing Rent Price Using Machine Learning Models. Mod Econ Manag, 2023; 2: 14.
T. P. Ogundunmade, A. O. Daniel, and A. M. Awwal, “Modelling Infant Mortality Rate using Time Series Models”, Int. J. Data. Science., vol. 4, no. 2, pp. 107-115, Dec. 2023.
Ogundunmade, T.P., Adepoju, A.A. (2023). Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model. In: Awe, O.O., Vance, E.A. (eds) Sustainable Statistical and Data Science Methods and Practices. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. doi:10.1007/978-3-031-41352-0_14.
Ayansola OA, Ogundunmade TP, Adedamola AO. Modelling Willingness to Pay of Electricity Supply Using Machine Learning Approach. Mod Econ Manag, 2022; 1: 9. doi:10.53964/mem.2022009
Ogundunmade TP, Adepoju AA, Allam A. Stock price forecasting: Machine learning models with K-fold and repeated cross validation approaches. Mod Econ Manag, 2022; 1: 2. doi:10.53964/mem.2022001
Ogundunmade TP, Adepoju AA. The performance of artificial neural network using heterogeneous transfer functions. Int J Data Sci, 2021; 2: 92-103. doi: 10.18517/ ijods.2.2.92-103.2021
Adepoju AA, Ogundunmade TP. Economic Growth and its Determinant: A cross country Evidence. Statistical Trans New Ser, 2019; 20: 69-84. doi: 10.21307/ stattrans-2019-015
Ogundunmade TP, Adepoju AA, Allam A. Predicting crude oil price in Nigeria with machine learning models. Mod Econ Manag, 2022; 1: 4. doi: 10.53964/mem.2022004
Ogundunmade TP, Adepoju AA. Modelling Liquefied Petroleum Gas Prices in Nigeria Using Time Series Machine Learning Models. Mod Econ Manag, 2022; 1: 5. doi:10.53964/mem.2022005