Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

Abstract

Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics and machine learning techniques. In this study, we investigate the methodology and performance evaluation of various machine learning algorithms for credit card fraud detection, emphasizing data preprocessing techniques and model effectiveness. Through thorough dataset analysis and experimentation using cross-validation approaches, we assess the performance of logistic regression, decision trees, random forest classifiers, Naïve Bayes classifiers, K-nearest neighbors (KNN), and artificial neural networks (ANN-DL). Key performance metrics such as accuracy, sensitivity, specificity, and F1-score are compared to identify the most effective models for detecting fraudulent transactions. Additionally, we explore the impact of different folds in cross-validation on model performance, providing insights into the classifiers' robustness and stability. Our findings contribute to the ongoing efforts to develop efficient fraud detection systems, offering valuable insights for financial institutions and researchers striving to combat credit card fraud effectively.