The complexity of financial fraud has been rising as a result of the rapid growth of digital transactions, online banking, and financial technologies. The existing system of fraud detection is based on textbook rules and manual audits that cannot be used to recognise emerging patterns of fraud on a real time basis. Artificial intelligence provides better opportunities to examine big financial data, identify abnormalities, and anticipate fraudulent activities in advance. In this work, a hybrid AI structure of machine learning, deep learning, and ensemble is suggested that would enhance the accuracy and reliability of early fraud detection. The hybrid model combines the use of the temporal pattern recognition, anomaly detection and classification models that enable detection of the known and new fraud patterns. Preprocessing of data such as normalization and imbalance use improves model performance. Experimental analysis proves that hybrid models are better than standalone models based on precision, accuracy and recall as well as speed of identifying fraud. Explainability methods enhance financial institution transparency and support decision making. The results underscore the significance of the hybrid artificial intelligence systems in enhancing the fraud prevention systems. The suggested model can help to increase financial stability, mitigate financial loss, and make it possible to implement proactive measures against fraud. Practical implications, study limitations, and research directions on the scalable artificial intelligence fraud detection systems are also discussed in the study.