Research on multi-source data fusion algorithm and optimization application for financial risk identification

With the increasing complexity of corporate financial activities and the diversification of data sources, the traditional financial risk identification methods are facing the problems of low accuracy, untimely response and low data utilization. In order to improve the accuracy and intelligence level of risk identification, this paper focuses on the research of multi-source data fusion technology, constructs the fusion algorithm system for financial risk identification, and discusses its optimization strategy and effect verification in practical application. Through feature modeling and unified coding of multiple types of data, such as financial statements, market quotations, public opinion information, etc., we design a fusion algorithm model suitable for heterogeneous data processing, and on this basis, we introduce methods such as deep learning and graph neural network to improve the fusion capability and prediction performance of the model. The experimental results show that the proposed fusion algorithm can effectively identify potential financial risks in a multi-source data environment, which is better than the traditional single data source model, and has strong robustness and generalizability. The research results provide a theoretical foundation and technical support for building an efficient and intelligent enterprise financial risk control system.