An Operational Risk Prediction Method for Corporate Financial Management Based on Big Data Analysis.

The information source of financial management operation data comes from financial statements, accounting records and other aspects. The amount of data is large, which increases the calculation amount of risk prediction algorithm. In order to reflect the business operation status and risk changes in a timely manner, provide comprehensive and multi-dimensional risk information, use the advantage of big data analysis to quickly process massive data, and propose a prediction method for business financial management operation risk based on big data analysis. Preprocess enterprise financial management and operation data through steps such as data cleaning, standardization, discretization, and fuzzy rough set mining to remove noise, redundancy, incomplete, and uncertain information from the data, and obtain reduced and dimensionality enterprise financial management and operation data; on this basis, select a variety of risk indicators, and use the gray system model to extract key enterprise financial management operational risk indicators. The enterprise financial management operational risk indicators are input into the SVM model as training samples. According to the constraints of the SVM classification model, the classification hyperplane is established, and the results of enterprise financial management operational risk prediction are obtained. Particle swarm optimization algorithm is used to optimize the predefined parameters and cost parameters of the kernel function in the SVM model to improve the accuracy of risk prediction. The experimental results show that this method has excellent feasibility and accuracy. It can not only accurately predict the risk level of the enterprise, but also the prediction results are highly stable and reliable. The method also has strong robustness and is suitable for long-term risk prediction. While the prediction time is short, it can also ensure a better balance between the generalization ability and performance of the model, and provide an efficient and practical risk management tool for enterprises. [ABSTRACT FROM AUTHOR]