A Credit Card Fraud Detection Algorithm Based on SDT and Federated Learning

With the rise of digital payment methods and the growth in financial transactions, the issue of credit card fraud has become increasingly severe. Traditional fraud detection methods are currently facing challenges such as poor model performance, difficulty in obtaining accurate results, and limitations in distributed deployment. These challenges stem from constantly evolving fraud strategies, higher volumes of transactions, and the complexity of the financial environment. This study proposes a credit card fraud detection algorithm based on Structured Data Transformer (SDT) and federated learning, which leverages the advanced capabilities of the Transformer model in deep learning. First, we organize credit card data into sequences and introduce a special, learnable token at the beginning of each sequence for classification purposes. Thanks to the attention mechanism of the Transformer, the model can automatically highlight important features in the data, significantly improving the accuracy of fraud detection. Second, addressing the complex financial environment and concerns about financial data privacy, we introduce a federated learning architecture to deploy the SDT model across different banks in a distributed manner. Momentum updates are used for model parameter updates during training, which enhance model performance and ensure data privacy between banks. Lastly, we conducted experimental validation on two financial datasets of different scales. The results on Dataset 1 and Dataset 2 show that our proposed SDT model surpasses traditional detection methods in terms of AUC-PR values (0.882, 0.816) and AUC-ROC values (0.982, 0.994). By integrating federated learning and deploying and testing the two datasets in a distributed environment, the AUC-PR values (0.884, 0.892) and AUC-ROC values (0.963, 0.998) can be further improved.