A QUANTITATIVE ANALYSIS OF AI-DRIVEN TRADE-FINANCE RISK ASSESSMENT MODELS FOR STRENGTHENING U.S. IMPORT–EXPORT OPERATIONS

Abstract

This study conducted a quantitative examination of AI-driven trade-finance risk assessment models to evaluate their effectiveness in predicting adverse transaction outcomes within U.S. import–export operations. Using a retrospective dataset of 1,248 trade-finance transactions, the analysis operationalized trade-finance risk through five measurable construct domains: counterparty risk, transaction risk, country and corridor risk, logistics risk, and compliance/documentation risk. Composite indices were developed for each construct and assessed for internal consistency, with Cronbach’s alpha values ranging from 0.79 to 0.88, indicating acceptable to strong reliability. A staged logistic regression framework was applied to estimate incremental explanatory power across predictor blocks while controlling for transaction amount, tenor, firm size, and corridor tier. Model performance improved progressively as constructs were added, with pseudo R² increasing from 0.092 in the controls-only model to 0.267 in the full model, and discrimination performance improving from an AUC of 0.681 to 0.812. Regression results showed that all five constructs were statistically significant predictors of adverse outcomes. Compliance and documentation risk exhibited the strongest effect (odds ratio = 2.64, p < 0.001), followed by counterparty risk (odds ratio = 2.32, p < 0.001) and transaction risk (odds ratio = 1.88, p < 0.001). Country and corridor risk (odds ratio = 1.67, p < 0.001) and logistics risk (odds ratio = 1.34, p = 0.026) also contributed independent explanatory value. Descriptive analysis indicated that between 21.6% and 34.7% of transactions exceeded predefined operational risk thresholds across constructs, with transaction risk showing the highest average score (mean = 0.51, SD = 0.19). Robustness checks across corridor tiers, industries, and firm-size segments confirmed stability of most construct effects, although logistics risk displayed partial sensitivity to segmentation. Overall, the findings demonstrated that AI-driven models integrating financial, transactional, contextual, operational, and compliance signals provided materially stronger predictive performance than baseline approaches, supporting the analytical value of multi-domain risk measurement for trade-finance decision systems.