The challenge of fraud detection, especially in credit card transactions, continues to grow as fraudsters adapt to increasingly sophisticated tactics. Traditional methods, including rule-based systems, are often limited by high false positives and poor adaptability to new fraud patterns. This study introduces a cutting-edge approach using an attention-based ensemble framework that synergizes the strengths of convolutional neural networks (CNNs), graph neural networks (GNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) models. A key innovation of this model is its confidence-driven gating mechanism, which dynamically combines the most reliable prediction, ensuring both accuracy and transparency. By incorporating dependent ordered weighted averaging (DOWA) and induced ordered weighted averaging (IOWA) operators, the model effectively integrates outputs from various classifiers, improving robustness. Additionally, SHAP-based feature selection reveals the most influential variables, providing deeper interpretability. Extensive evaluations across three diverse benchmark datasets demonstrate the superior performance of this framework, surpassing individual classifiers and existing ensemble models in both balanced and highly imbalanced settings. This research paves the way for more reliable and transparent fraud detection systems, capable of adapting to evolving fraud tactics in real-world financial environments.