An improved machine learning technique for credit card fraud detection

Credit Card Fraud (CCF) detection is a major challenge in financial security, especially in detecting unauthorized transactions. As fraudsters' patterns increase, traditional methods face challenges in detecting them. Hence, this paper proposes a Sea Lion - Self-Supervised Network (SL-SSNet) to improve detection accuracy. This research study aims to enhance performance by optimizing data quality feature extraction and achieving better results in fraud detection. The innovative approach for CCF detection using a hybrid optimization model combines the strengths of Sea Lion optimization and Self-Supervised Networks to improve both model accuracy and performance. Initially, the CCF dataset is collected from Kaggle. Then the data goes through a pre-processing phase where irrelevant data points, noise, and low-quality data are removed. The relevant data is selected in the next phase. Feature extraction is performed to select the most important and influential features related to fraud detection. The final phase is prediction and performance evaluation. The results show that the SL-SSNet model performs better than other methods in fraud detection. Specifically, the model achieved 99.98% accuracy, 82.46% precision, 97.23% recall, and 89.97% F1-score. These results prove the effectiveness and robustness of SL-SSNet in detecting fraudulent transactions.