Machine Learning in Action: Supervised Learning Models for Classifying Credit Card Fraud

Financial institutions face a relentless battle against fraudulent credit card transactions. Machine learning (ML) offers a promising approach for tackling this challenge. This paper explores six supervised machine learning models (Support Vector Machine (SVM), Random Forest, Naive Bayes, K-Nearest Neighbours (KNN), XGBoost, and LIGHT GBM) for detecting credit card fraud using a benchmark dataset. We explore the impact of dimensionality reduction with Principal Component Analysis (PCA) and data balancing with Generative Adversarial Networks (GANs) on imbalanced class distributions. The assessment relies on metrics such as accuracy, precision, recall, and F1-score. The results indicate that Random Forest demonstrates the best overall performance, especially when paired with GAN-based data balancing techniques. This study highlights the importance of addressing class imbalance and exploring dimensionality reduction techniques for enhancing credit card fraud detection capabilities.