Leveraging Ensemble Machine Learning: A Comparative Analysis for Enhanced Credit Card Fraud Detection

Credit card fraud poses a significant challenge in the financial sector, leading to monetary losses, reputational risks, and decreased consumer trust. Traditional fraud detection methods often fail to keep pace with evolving fraudulent tactics. This study explores real-time fraud detection by evaluating various machine learning techniques, including ensemble methods, KNN, decision trees, logistic regression, and SVM. Using a publicly available dataset that has both fraudulent and legitimate transactions, the models were assessed in the standard metrics while addressing the class imbalance issue. Key performance metrics such as recall, accuracy, precision, and F1-score were used for the evaluation. The study outlines the strengths and weaknesses of each model and suggests advanced deep learning techniques for further enhancement of Fraud Transaction Detection while taking the class imbalance in account. This study underscores the potential of machine learning in improving fraud detection by optimizing precision and recall, effectively reducing errors in practical applications.