Construction of an International Trade Financial Risk Assessment and Prediction Model Based on Big Data Analysis

Background: International trade promotes economic growth across nations, while imposing financial risks of currency fluctuations, credit defaults, and market volatility. Although conventional methods of risk evaluation have served well in the past, they are, however, unable to provide risky international trade answers under the dynamic conditions at present. Objective: This study aims to develop data-driven risk assessment and prediction models for financial risks in international trade, with an emphasis on the China trade regime dominated by finance. The purpose is to maximize prediction accuracy and to provide pragmatic risk management solutions. Methods: The study proposed a hybrid method capable of characterizing complex nonlinear correlations by a DNN and, subsequently, estimating prediction outputs with an LR model for enhanced interpretability. Training models on the International Trade and Finance Dataset are augmented by macroeconomic indicators; preprocessing is performed via statistical imputation, feature normalization, and one-hot encoding. Results: With values of 0.9670, 0.0408, 0.0322, and 0.0017 awarded to R², RMSE, MAE, and MSE, respectively, the model stands out as the most capable and accurate in measuring financial risk. However, this hybrid model marries complex features with interpretable features, thereby paving the way for an exquisite instrument for risk assessment. Conclusion & Implications: This study aims to develop a solid framework for predicting financial risks in international trade that will aid financial institutions in decision-making and in developing policies. The findings may be applied to ongoing financial stability assessments for trade risk management.