ENHANCING CREDIT CARD FRAUD DETECTION IN BANKING USING NEURAL NETWORKS

Abstract

Credit card fraud has emerged as one of the most significant challenges faced by financial institutions due to the rapid growth of digital banking and online transactions. Traditional machine learning algorithms often struggle to maintain high detection accuracy while minimizing false positives, particularly in highly imbalanced transaction datasets. This paper proposes an interpretable deep learning framework based on the TabNet architecture for enhancing credit card fraud detection in banking systems. Unlike conventional neural networks, TabNet employs sequential attention mechanisms to perform feature selection dynamically, enabling superior learning from structured tabular transaction data while providing model interpretability. The proposed methodology includes comprehensive data preprocessing, missing value handling, feature scaling, class imbalance correction using SMOTE, and hyperparameter optimization. Experimental evaluation is conducted on the publicly available European Credit Card Fraud Detection dataset. Performance is evaluated using Accuracy, Precision, Recall, F1-score, ROC-AUC, PR-AUC, and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that TabNet outperforms traditional neural networks and popular machine learning algorithms by achieving high fraud detection accuracy with significantly reduced false alarms while maintaining excellent interpretability suitable for financial regulatory requirements. Recent studies have similarly reported strong performance of TabNet for banking fraud detection, highlighting its suitability for structured financial datasets and explainable AI applications.