  Credit card has become popular mode of payment for both online and offline
purchase, which leads to increasing daily fraud transactions. An Efficient
fraud detection methodology is therefore essential to maintain the reliability
of the payment system. In this study, we perform a comparison study of credit
card fraud detection by using various supervised and unsupervised approaches.
Specifically, 6 supervised classification models, i.e., Logistic Regression
(LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree
(DT), Random Forest (RF), Extreme Gradient Boosting (XGB), as well as 4
unsupervised anomaly detection models, i.e., One-Class SVM (OCSVM),
Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), and Generative
Adversarial Networks (GAN), are explored in this study. We train all these
models on a public credit card transaction dataset from Kaggle website, which
contains 492 frauds out of 284,807 transactions. The labels of the transactions
are used for supervised learning models only. The performance of each model is
evaluated through 5-fold cross validation in terms of Area Under the Receiver
Operating Curves (AUROC). Within supervised approaches, XGB and RF obtain the
best performance with AUROC = 0.989 and AUROC = 0.988, respectively. While for
unsupervised approaches, RBM achieves the best performance with AUROC = 0.961,
followed by GAN with AUROC = 0.954. The experimental results show that
supervised models perform slightly better than unsupervised models in this
study. Anyway, unsupervised approaches are still promising for credit card
fraud transaction detection due to the insufficient annotation and the data
imbalance issue in real-world applications.
