A Big Data Stream-Driven Risk Recognition Approach for Hospital Accounting Management Systems

Abstract

This work is confronted with hospital accounting management systems where business volume is usually large and trivial. While designing system prototype and processing algorithms, it is required to integrate realistic big data stream as the main factors for consideration. Because of such point, currently, there still lacks mature solutions for accounting risk recognition in such scenes. Combined with the micro service management technology of data flow, this paper puts forward the risk identification mode and cloud Data integrity verification algorithm for the purpose. Compared with traditional single user authentication techniques, this method has a significantly higher accuracy in hospital data analysis compared to comparative algorithms. At the same time, its error has been reduced. The multi-user parallel authentication algorithm further improves the computational efficiency of the authentication process while ensuring the integrity of data files and reducing the average time. Finally, we also make some empirical analysis on realistic data to testify performance of the proposed technical framework. The results show that the proposal is well suitable for digital risk recognition in hospital accounting management systems. And the recognition accuracy of the proposal can achieve 98%, and is about 22% higher than comparison methods.