Research on Risk Warning System for Insurance Company Based on Neural Network

With the rapid increase of data in the insurance industry, traditional risk assessment methods are facing limitations, while neural network technology has attracted much attention because of its powerful pattern recognition ability. In this paper, RBF neural network is used to improve the accuracy and efficiency of risk prediction of insurance companies. Through data acquisition and cleaning to ensure data quality, we extract and select features to identify key risk factors. We design and implement the RBF neural network model, including determining the network structure and parameter settings. Finally, the system performance is evaluated by model training and testing. The results show that the accuracy of the constructed model reaches 95. 2% on the training set and 92. 8% on the test set, showing good generalization ability and risk prediction effect. This study provides an effective risk management tool for insurance companies, which helps to optimize risk control strategies and improve the quality of insurance services.