From Trust to Transparency: A Systematic Qualitative Review of Regulatory, Ethical, and Organizational Challenges in AI-Driven Letter of Credit Automation - Moh Zalmi Kahardani; Erwin Santosa (International Journal of Business, Marketing, Economics & Leadership (IJBMEL), 2026)
This study presents a systematic qualitative literature review examining regulatory, ethical, and organizational challenges in the adoption of artificial intelligence (AI) for letter of credit (LC) automation in trade finance. Synthesizing prior interdisciplinary research, the review finds that AI technologies—such as machine learning and natural language processing—offer substantial efficiency, accuracy, and transparency gains in documentary credit examination. However, these benefits are constrained by regulatory uncertainty stemming from legacy legal frameworks, ethical concerns related to algorithmic opacity, bias, and accountability, and organizational challenges involving trust, governance, and workforce readiness. The findings highlight that AI-driven LC automation constitutes a socio-technical transformation rather than a purely technological upgrade, requiring alignment between evolving regulation, responsible AI governance, and organizational change management. This study contributes to the trade finance and digital transformation literature by providing an integrated perspective on how transparency can be institutionalized without undermining trust in automated financial decision-making