A Survey on Credit Card Fraud Detection using Deep Learning Model

The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies. The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving fraud dataset handling of class imbalance issues. The method which is known as federated learning currently attracts attention because it helps institutions to collaborate on separate model training without exposing their actual data. Problems persist with the current development of fraud detection models since they need adaptable models for various datasets while also requiring interpretation capabilities and functionality that adapts to changing deceit patterns. The development of privacy-preserving methods for fraud detection must continue because they need to achieve sufficient efficiency and security standards for real-time applications. Problems with scalability in addition to unclear detection methods and adaptability require new research directions that fill existing knowledge gaps.