As internet payment methods have evolved quickly, so have the number and complexity of fraudulent activities. This has made money transfers happen faster. Traditional fraud detection algorithms often have problems with extreme class imbalance, concept drift, and the need to make decisions in real time. These problems might cause them to miss or incorrectly identify unusual fraud patterns. Due to these challenges, this work proposes a Dual View Rare Pattern Autoencoder (DVRPA) for the real-time detection of financial fraud in high-speed transaction environments. The proposed DVRPA architecture employs a dual-view learning methodology to acquire both transaction-level behavioral patterns and stream dynamics within a temporal context. Two autoencoders are trained at the same time: one looks at how transactions change over time to find new fraud patterns, and the other focuses on reconstructing intrinsic features to find unusual changes. A fusion-based anomaly scoring method combines the reconstruction errors from both points of view. This makes it easy to find subtle and concealed fraudulent patterns. The model is built to easily handle streaming data, using adaptive thresholding and lightweight updates to keep latency low and scalability high. When evaluated on huge quantities of financial transaction data, DVRPA was far better than typical ML and single DL models at recall, F1-score, detection latency, and precision. The results show that the system is more sensitive to rare fraud events, even when there is a lot of class imbalance and the distribution of transactions changes. The false-positive rate is very low.  According to a lot of tests, DVRPA often trumps state-of-the-art algorithms like OC-SVM, LSTM-AE, and Deep SVDD on huge, very skewed financial transaction datasets. The recommended framework works for high-speed transaction streams and has a detection latency of less than a millisecond. It improves the F1-score by 8–12%, the recall by 10–15%, and the false positive rate by 6–9%. Finally, the suggested DVRPA is a good choice for high-speed transaction streams that need to find financial fraud in real time. Modern financial systems can benefit from the model's better capacity to find things, adapt, and run more smoothly. This is made possible by using both dual-view representations and rare pattern learning together.