With the rapid development of financial technology, financial fraud is now extremely complex and hidden, and the previous single detection method cannot deal with it well. This paper proposes a cross-platform financial fraud detection system based on ensemble learning to improve the accuracy and real-time detection of the system based on the effective integration of multiple machine learning algorithms. The system architecture mainly includes data source access, data collection and transmission, etc. The results of this study show that this system can effectively process multi-source heterogeneous data, detect fraud in real time, automate data processing, etc., and has high scalability and flexibility, and can be widely applied to major financial platforms or institutions. At the same time, provide them with a reliable and consistent fraud detection service. In addition, the architecture design of the system can also effectively expand future functions, and realize continuous optimization of performance, so as to continuously adapt and meet the changing business needs.