Supervised and Unsupervised approach of Machine Learning: A Comparative Study for Credit Card Fraud Detection Across Diverse Datasets

Abstract

Credit card fraud detection is a challenging task due to the increase in digital transactions, evolving fraud patterns, and severe class imbalance present in real-world datasets. Although several comparative studies have been done in this domain, many of them are limited to a single dataset and primarily focus on predictive performance. This study presents a comparative evaluation of Supervised and Unsupervised Machine Learning algorithms for credit card fraud detection across five diverse Kaggle datasets with varied sizes, class distributions, and feature representations, including PCA-transformed numerical datasets and real-world-like datasets having numerical and categorical features. Eleven Supervised Machine Learning algorithms, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Naïve Bayes, K-Nearest Neighbors, LightGBM, XGBoost, CatBoost, and AdaBoost, together with four Unsupervised Machine Learning algorithms, namely K-Means, DBSCAN, Isolation Forest, and One-Class SVM, were evaluated using a consistent experimental framework. To keep alignment with real-world transaction environments, no sampling, resampling, or class-balancing techniques were applied. Performance was assessed using Accuracy, Precision, Recall, F1-score, ROC AUC, PR AUC, MCC, learning time, and execution time. Experimental results show that supervised ensemble methods consistently outperformed unsupervised approaches across all datasets. XGBoost and CatBoost achieved consistently strong fraud detection performance in terms of ROC AUC, PR AUC, and MCC across diverse datasets while keeping reasonable computational efficiency. Their ability to achieve high fraud detection performance under severe class imbalance makes them suitable for practical fraud detection systems. The findings provide practical insights into the robustness, scalability, and suitability of machine learning algorithms for credit card fraud detection under varying real-world data conditions that helps researchers and scientist for its better understandings.