Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection

Farouk, Maged; Shaker, Nashwa; AbdElminaam, Diaa S; Elrashidy, Omnia; Ghorab, Nada; Hany, Jevana; Amr, Alaa; Adel, Omar; Saad, Kriols; Ali, Khaled; Elazab, Reda

doi:10.21608/jocc.2024.339929

|
|
|
How to cite
RIS EndNote BibTeX APA MLA Harvard Vancouver
|
Share

	Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection
Article 9, Volume 3, Issue 1, January 2024, Page 116-131 PDF (1.85 MB)
Document Type: Original Article
DOI: 10.21608/jocc.2024.339929
View on SCiNiTO
Authors
Maged Farouk¹; Nashwa Shaker¹; Diaa s AbdElminaam ²; Omnia Elrashidy¹; Nada Ghorab³; Jevana Hany³; Alaa Amr⁴; Omar Adel³; Kriols Saad⁴; Khaled Ali³; Reda Elazab¹
¹Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
²Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt
³Department of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt
⁴aDepartment of Business Information Systems, Faculty of Business, Alamein University, Alamein, Egypt
Abstract
Online payment fraud detection is crucial for safeguarding e-commerce transactions against sophisticated fraudsters who exploit system vulnerabilities. This paper proposes an efficient framework for predicting online payment fraud, employing six diverse machine learning algorithms, namely constant, CN7Rule induction, KNN, Tree, Random Forest, Gradient boosting, SVM, Logistic regression, Naive Bayes, Ada boost, Neural network, and stochastic gradient descent, on three distinct datasets. The gradient-boosting algorithm consistently outperformed others through rigorous testing, achieving an impressive accuracy rate of 99.7%. This algorithm demonstrated resilience across various testing scenarios, establishing itself as the most effective online payment fraud detection solution. With the highest accuracy score of 99.7% in all testing phases, gradient boosting is optimal for preemptive measures against fraudulent activities in electronic transactions, providing a robust defense mechanism for e-commerce platforms.
Keywords
Online payment fraud; Machine-Learning; gradient boosting; CN2Rule Induction; fraud deduction


References
[1] 8ir5Sakharova, I. (2012, June). Payment card fraud: Challenges and solutions. In 2012 IEEE international conference on intelligence and security informatics (pp. 227-234). IEEE [2] Almazroi, A. A., & Ayub, N. (2023). Online Payment Fraud Detection Model Using Machine Learning Techniques. IEEE Access, 11, 137188-137203 [3] El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing. [4] Minastireanu, E. A., & Mesnita, G. (2019). An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection. Informatica Economica, 23(1). [5] Nasr, M. H., Farrag, M. H., & Nasr, M. M. (2022). A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway. International Journal of Advanced Computer Science and Applications, 13(5). [6] Fang, Y., Zhang, Y., & Huang, C. (2019). Credit Card Fraud Detection Based on Machine Learning. Computers, Materials & Continua, 61(1). [7] Mijwil, M. M., & Salem, I. E. (2020). Credit card fraud detection in payment using machine learning classifiers. Asian Journal of Computer and Information Systems (ISSN: 2321–5658), 8(4). [8] Adepoju, O., Wosowei, J., & Jaiman, H. (2019, October). Comparative evaluation of credit card fraud detection using machine learning techniques. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE. [9] Isabella, S. J., Srinivasan, S., & Suseendran, G. (2020). An efficient study of fraud detection system using Ml techniques. Intelligent Computing and Innovation on Data Science, 59. [10] Pumsirirat, A., & Liu, Y. (2018). Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. International Journal of advanced computer science and applications, 9(1). [11] Corballis, M. C., & Nagourney, B. A. (1978). Latency to categorize disoriented alphanumeric characters as letters or digits. Canadian Journal of Psychology/Revue canadienne de psychologie, 32(3), 186. [12] Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. [13] Asim, M., & Zakria, M. (2020). Advanced kNN: A Mature Machine Learning Series. arXiv preprint arXiv:2003.00415. [14] 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. [15] Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., ... & Cheng, C. Y. (2020). Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology, 122, 56-69. [16] Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, 50(2), 491-500. [17] Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert systems with applications, 134, 93-101. [18] Ding, S., Su, C., & Yu, J. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial intelligence review, 36, 153-162. [19] Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003. [20] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66. [21] Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361.
Statistics Article View: 979 PDF Download: 816