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This thesis examines the integration of Artificial Intelligence (AI) into global sanctions management, a field currently dominated by manual processes and semi-automated systems that are inefficient and error-prone. With the increasing complexity and volume of international financial transactions, traditional approaches to sanctions compliance are no longer adequate to meet the demands of dynamic global regulations and enforcement challenges. The primary aim of this research is to explore how AI can revolutionize sanctions management by enhancing accuracy, speed, and adaptability in compliance operations. Key findings reveal that AI can significantly reduce the reliance on human intervention, thereby decreasing operational costs and minimizing errors. AI-enhanced systems are shown to improve the consistency and reliability of compliance measures, effectively manage large volumes of data, and swiftly adapt to regulatory changes. The research highlights that integrating AI into sanctions management not only bolsters compliance efficiency but also strengthens the overall risk management framework of financial institutions. The significance of this research lies in its potential to guide financial institutions in implementing AI solutions that can transform their sanctions compliance frameworks. By providing a detailed roadmap for the adoption of AI, the study contributes to broader efforts to enhance global financial security and compliance, positioning AI as a pivotal technology in the evolution of global sanctions management.
Bing Hu (Sat,) studied this question.