Financial fraud is a serious threat in online banking, requiring sophisticated and responsive fraud detection systems. Rule-based systems and machine learning algorithms are at a loss in dealing with high-dimensional financial information, resulting in higher false positives and undetected fraudulent activities. To tackle these issues, this research recommends a hybrid framework for fraud detection combining Variational Autoencoders (VAE) for detecting anomalies and Transformer networks for fraud classification. VAE is trained on learning the distribution of valid transactions and identifies outliers according to the errors in reconstruction, whereas Transformer neural network employs self-attention processes to make class predictions with high accuracy. Performance is also analyzed on PaySim dataset to identify 99.48% accuracy, 99.39% precision, 99.55% recall, and minimum 0.599% false positives, clearly exceeding the efficiency of conventional machine learning classifiers. The framework under consideration increases fraud prevention mechanisms through adaptive learning functionality, scalability, and real-time transactional monitoring.