Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection
Maged Farouk, Nashwa Shaker, Diaa Salama AbdElminaam, Omnia Elrashidy, Nada Ghorab, Jevana Hany, Alaa Amr, Omar Adel, Kriols Saad, Khaled A. Ali, Reda Elazab
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.