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

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	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
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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
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