자금세탁방지 연구 동향 분석: 생성형AI를 활용한 토픽 모델링을 중심으로

This study analyzes research trends in Anti-Money Laundering (AML) by applying Latent Dirichlet Allocation (LDA) and topic network centrality analysis to 186 AML-related articles published between 2003 and 2025. Six major topics—compliance, financial, bank, international, technology, and enforcement—were identified and reorganized into three upper-level structures: Regulatory and International Governance, Financial Institutional Operations, and Enforcement and Technological Innovation. Network centrality analysis shows that the financial and enforcement topics function as dual structural hubs, exhibiting the highest degree and betweenness centrality. These findings indicate that AML research is organized around financial operations, while enforcement and technological innovation act as expansion and mediation axes. Generative AI was also employed to refine topic interpretation and improve semantic coherence. The results demonstrate that AML research has evolved from regulation-centered discourse toward a multilayered structure integrating governance, operational practice, and digital transformation. From a policy perspective, the findings highlight the importance of strengthening coordination between financial institutions, regulatory authorities, and technological innovation in order to enhance the effectiveness of AML governance in the digital financial environment.