Research on Intelligent Early Warning Algorithm for Risk Assessment of Digital Supervision Business Process

This study proposes an intelligent early warning model based on decision risk tree and random forest algorithm. With the increasing complexity of digital supervision business processes, traditional supervision methods are difficult to effectively identify potential risks. The risk factors of every step are summarized into nodes, and a risk evaluation frame is built by the decision risk tree, and the integrated learning of the random forest algorithm is used to analyze and my multidimensional data. The system algorithm design process includes steps such as data preprocessing, feature selection, and risk weight calculation to ensure that the model has good generalization ability. In the simulation experiment, multiple actual digital supervision business process data sets were selected for verification. The results show that the intelligent early warning model designed in this study can effectively identify high-risk nodes in the business process and generate accurate early warning information, with a simulation accuracy of more than 95%. In addition, by comparing the model performance under different parameters, the robustness and practicality of the proposed method are further verified.