Abstract: Credit card fraud remains a major challenge for financial institutions due to the growing number of online transactions and the sophistication of fraudulent techniques. Traditional machine learning methods often struggle with class imbalance and lack contextual understanding. In this study, we propose a credit card fraud detection framework leveraging Transformer-based architectures integrated with transfer learning. The model is fine-tuned on transaction data to detect fraudulent activities effectively. Experimental results demonstrate improved performance in comparison to conventional classifiers, suggesting that Transformer-based models are well-suited for time-series and sequential data in fraud detection scenarios.
Keywords: Credit Card Fraud Detection, Transformers, Transfer Learning, Deep Learning, Anomaly Detection, Financial Security
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DOI:
10.17148/IJARCCE.2025.14590
