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International trade with unfamiliar stakeholders across countries presents challenges related to trust, payment security, and regulatory compliance. Letters of Credit (LC) mitigate these risks by establishing trust and ensuring secure transactions. However, existing LC processes remain heavily manual, labor intensive, and error prone, leading to significant delays, high costs, and limited scalability and transparency. The integration of Artificial Intelligence (AI) presents a promising solution. This study develops a roadmap for AI adoption in trade finance, focusing on automating LC document examination and addressing key research gaps in digitalization and hybrid AI integration. A qualitative approach, supported analysis of secondary data, is used to create an AI adoption framework. Several case studies of firms undergoing digital transformation were analyzed to identify trade finance bottlenecks, AI’s role in addressing them, and critical elements in implementing a human in the loop approach to balance automation with risk management. Moreover, efficient task assignment for human verification is essential to fully harness AI’s potential while balancing time savings with risk mitigation. This research examines the underexplored challenge of optimizing the hybrid process of AI-assisted LC examination aiming to minimize examination risk and maximize checker capacity utilization by offering practical strategies for improvement. Through data-driven research collaboration with international banks, an optimization model was developed to assign review tasks for LC documents indexed by AI based on their criticality and discrepancies to human checkers. The model also considers monetary value, checker expertise, and availability factors. Real case studies evaluated improvements over baseline practices, objectives prioritization, trade-off analysis, and varying supply–demand scenarios. The model achieved a 96.6% reduction in risk compared to baseline AI examination and a 97.9% reduction compared to baseline human examination, while maintaining 73% utilization. Utilization rates ranged from 34% for risk-focused strategies to 73% for efficiency-driven approaches, providing flexibility for resource allocation aligned with organizational priorities. AI can significantly enhance trade finance efficiency, but sole reliance on automation is risky. A hybrid AI-human approach ensures accuracy and compliance.
Using the proposed AI-optimization framework, research findings, and the developed roadmap, this study offers strategic guidance for AI implementation, actionable insights for managers, and recommendations for researchers exploring future directions in AI-driven trade finance.
