AI Enabled Risk Management in International Logistics Networks

Abstract

International logistics networks are increasingly exposed to operational, financial, geopolitical, customs, cyber, and environmental risks. Effective risk management is therefore essential for reducing shipment delays, controlling logistics costs, and improving supply chain reliability. This study aims to examine how artificial intelligence-enabled classification models can support risk prediction in international logistics networks. For this research, a primary dataset was developed containing 3,000 shipment records with 30 variables, including shipment route, transport mode, cargo value, cargo weight, transit time, delay days, weather risk, political risk, customs risk, port congestion, cyber risk, insurance cost, total logistics cost, and actual risk level. The study applied supervised machine learning methods using a 70% training, 15% validation, and 15% testing split. Three AI-based classifiers—Random Forest, XGBoost, and LightGBM—were used to classify shipment risk into Critical, High, and Medium levels. Model performance was evaluated using accuracy, precision, recall, F1-score, and classification reports. The results show that Random Forest and XGBoost achieved the highest accuracy of 91%, while LightGBM achieved 90% accuracy. Random Forest and XGBoost performed strongly in identifying Critical risk shipments, both reaching an F1-score of 0.97 for the Critical class. LightGBM showed better recall for Medium risk shipments, indicating stronger detection of minority risk cases. Overall, the findings suggest that AI-enabled models can effectively support risk prediction and decision-making in international logistics networks. The study concludes that machine learning can improve proactive risk management, reduce uncertainty, and enhance logistics network resilience.