In recent years, credit card transaction fraud has resulted in massive losses for both consumers and banks. Subsequently,
both cardholders and banks need a strong fraud detection system to reduce cardholder losses. Credit card fraud detection
(CCFD) is an important method of fraud prevention. However, there are many challenges in developing an ideal fraud
detection system for banks. First off, due to data security and privacy concerns, various banks and other financial
institutions are typically not permitted to exchange their transaction datasets. These issues make traditional systems find it
difficult to learn and detect fraud depictions. Therefore, this paper proposes federated learning for CCFD over different
frameworks (TensorFlow federated, PyTorch). Second, there is a significant imbalance in credit card transactions across all
banks, with a small percentage of fraudulent transactions outweighing the majority of valid ones. In order to demonstrate
the urgent need for a comprehensive investigation of class imbalance management techniques to develop a powerful model
to identify fraudulent transactions, the dataset must be balanced. In order to address the issue of class imbalance, this study
also seeks to give a comparative analysis of several individual and hybrid resampling techniques. In several experimental
studies, the effectiveness of various resampling techniques in combination with classification approaches has been compared.
In this study, it is found that the hybrid resampling methods perform well for machine learning classification models
compared to deep learning classification models. The experimental results show that the best accuracy for the Random
Forest (RF); Logistic Regression; K-Nearest Neighbors (KNN); Decision Tree (DT), and Gaussian Naive Bayes (NB)
classifiers are 99,99%; 94,61%; 99.96%; 99,98%, and 91,47%, respectively. The comparative results show that the RF
outperforms with high performance parameters (accuracy, recall, precision and f score) better than NB; RF; DT and KNN.
RF achieve the minimum loss values with all resampling techniques, and the results, when utilizing the proposed models on
the entire skewed dataset, achieved preferable outcomes to the unbalanced dataset. Furthermore, the PyTorch framework
achieves higher prediction accuracy for the federated learning model than the TensorFlow federated framework but with
more computational time. |
