Cross-border trade risk prediction model based on support vector machine

Abstract

Exchange rate fluctuations in cross-border trade often increase the difficulty of risk management for enterprises, and traditional forecasting methods are difficult to accurately assess risk levels, thereby affecting the optimization of financial decisions. Therefore, this article designed a cross-border trade risk prediction model based on Support Vector Machines (SVM), mainly to solve the problem of financial uncertainty caused by exchange rate fluctuations. The study used SVM model to extract and analyze features from exchange rate data, economic indicators, and other related data, mapped the data to high-dimensional space using kernel function, and optimized hyperparameters using Bayesian optimization to achieve accurate prediction of cross-border trade risks. The experimental results showed that the accuracy of the designed model in high, medium, and low-risk classification was 92%, 81%, and 89%, respectively, with an average absolute error between 0.09 and 0.13, demonstrating high prediction accuracy and model stability. This model can effectively distinguish different risk categories and performs very well in handling high-risk and low-risk samples. The model also performs well in predicting stability and adaptability to exchange rate fluctuations, providing a reasonable reference range for risk management of cross-border trade enterprises.