AI-Driven Predictive Modeling of Export Bill Settlement: Analyzing Importer Country, Bank, and Payment Behavior

Abstract

Settlement of export bills within a reasonable period is crucial to ensure that there is liquidity and reduced risks in global trade. In this work, an AI-based quantitative model is created to forecast the export bill payment settlement rates, Early, On-Time, and even the slowest- Delays- through a set of variables, like the Country of import, Bank of importer, and their practices of paying bills, as well as financial and time-related indicators. Two thousand confirmed transactions (n=2,000) were tested with the help of Random Forest and XGBoost algorithm with the assistance of data preprocessing, feature engineering, and z-score normalization. The descriptive statistics indicate that the number of transactions settled early, which was 67.13 percent, the number of transactions delayed was 19.95 percent, and the number of transactions that settled on time was 12.91 percent, with a mean delay of 4.6 days, a standard deviation of 12.3, and a median of 1.0 day. The XGBoost model became the highest predictive accuracy that was achieved (89%), more than the random forest. The analysis of feature importance indicated that the most significant predictors of the settlement timing were importer payment behavior, banking efficiency, and country risk. The paper establishes that financial settlements can be effectively predicted by using the power of AI-based predictive-based modeling, and this can be applied by exporters, banks, and policy makers. It is further facilitated by having risk segmentation (Green, Yellow, Red), which can actively intervene in the possible delays. Finally, the study justifies the incorporation of the idea of the use of AI-powered analytics into trade finance to facilitate better decision-making, risk management, and operational efficiency in international exporting deals.