Research on International Trade Financial Risk Identification Based on Gbdt-Xgboost Algorithm

Abstract

This paper employs the GBDT-XGBoost algorithm to explore the characteristics and identification strategies of international trade financial risks. Initially, historical data related to international trade, including import and export volumes, exchange rate fluctuations, and credit ratings among other multidimensional indicators, are collected using data mining techniques. Subsequently, the data undergo preprocessing and feature selection to extract effective risk factors. The study constructs a model based on a multi-layer GBDT fused with XGBoost to enhance the accuracy of identifying international trade financial risks. The model's parameters are optimally matched using Grid Search technology. Empirical tests conducted on historical datasets from various domestic and international financial institutions demonstrate that the model can predict trade financial risks with high accuracy. Compared to traditional risk assessment methods, it exhibits better resistance to overfitting and improved risk identification performance. Finally, based on the predictive outcomes of the model, international trade financial risk warning strategies are proposed, which hold significant practical importance in preventing major financial events. This research provides new analytical tools and decision-support systems for international trade risk management, playing an active role in strengthening risk prevention measures for international trade enterprises.