Construction of Cross-Border Trade Supply Chain Financial Risk Assessment Model Based on Information System Intelligent Algorithm

Against the backdrop of deepening globalization, cross-border trade continues to expand. Supply chain finance, as a key financial instrument supporting cross-border trade, faces increasingly complex and diverse risks due to the political, economic, and cultural factors encompassing multiple countries and regions. Currently, traditional risk assessment methods suffer from issues such as delayed response to dynamic risks and a single assessment dimension, making them inadequate for accurately assessing cross-border trade supply chain finance risks. This article first identifies the types and characteristics of cross-border trade supply chain finance risks and collects multidimensional risk data using information systems. It then introduces intelligent algorithms (such as neural networks and random forests) to construct a risk assessment model. This model improves assessment accuracy through data preprocessing, feature selection, model training, and optimization. Finally, it conducts experimental validation using real-world cross-border trade supply chain finance data. The results demonstrate that the proposed model achieves a standard deviation of only 0.08 in early warning values, significantly lower than the 0.15 of the random forest model, effectively providing decision support for financial institutions.