Consumers and businesses face financial risks because of the increase in credit card fraud brought on by the boom in digital transactions. Traditional fraud detection technology can’t keep up with evolving fraud strategies, which leads to high false positives and undetected fraud. This paper proposes a hybrid deep learning system that integrates Autoencoders, Conv1D, SMOTE, and LSTM to increase the accuracy of fraud detection. SMOTE addresses class imbalance, Autoencoders extract complex transaction patterns, Conv1D detects local dependencies, and LSTM captures long-term temporal correlations. Class imbalance is addressed by SMOTE, complicated transaction patterns are extracted by Autoencoders, local dependencies are detected by Conv1D, and long-term temporal correlations are captured by LSTM. When compared to traditional models, experimental results on the European Credit Card Dataset demonstrate improved precision, recall, and F1-score. The results highlight how crucial hybrid deep learning is to create adaptive fraud detection systems that can react to new fraud trends. To improve financial security, future work will concentrate on real-time deployment and enhancing model interpretability.