This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in-depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM-based FRM model; the Logistic regression-based FRM model can efficientlyclassify financial risks; theinformation fusion-based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM-based and Logistic regression-based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion-based FRM modelbeats SVM-based and Logistic regression-based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion-based FRM is better than the SVM-based and Logistic regression-based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM-based FRM model and Logistic regression-based FRM model can well classify enterprise financial risks, with relatively high accuracy.

A BP neural network-based model is proposed to study corporate financial risk analysis and internal accounting management. Using MATLAB software and the BP neural network model, it is possible to obtain enterprise financing risk situations over a period by simulating and predicting enterprise financing risks by creating an early warning model for enterprise financing risks. Finally, from the point of view of the company's internal and external operations, the company's financial risk prevention measures and proposals are proposed to improve the financing efficiency of the companies and to prevent financial risks. This study predicts the financing risk of companies listed on the Mongolian Stock Exchange and analyzes the causes of the risk status. According to the test results, the learning speeds for successive substitutions are as follows: 0.005, 0.01, 0.02, 0.03, and 0.04. Finally, it was found that the error was minimal and the stability was best when the learning speed was exactly 0.01. The error is 0.0031011, and the step size is 157, which is only slightly lower than the target error value, which indicates that the learning speed is good. In addition, the novelty of this study is the use of the BP neural network model to conduct an early warning study of corporate financial risks. The BP neural network assessment model for corporate lending risk in this document is highly accurate. In addition to providing theoretical insights to researchers, it can be a good tool for banks to realistically assess the credit risk of SME supply chain financing.