<![CDATA[Trade-Based Money Laundering (TBML) is one of the most difficult forms of cross-border money laundering to detect, as it operates through complex international trade mechanisms. This practice has the potential to erode state revenues, undermine economic stability, and threaten the integrity of the global financial system. Amidst these dynamics, the utilization of technology has become key to strengthening TBML prevention efforts. This study aims to map the adoption of technology in global TBML mitigation practices and to assess Indonesia's readiness for its implementation. The research methodology combines a Systematic Literature Review (SLR) following PRISMA guidelines with semi-structured interviews conducted with key stakeholders from the Directorate General of Customs and Excise (DGCE) and academia. The findings indicate that technologies such as Artificial Intelligence, Machine Learning, Blockchain, and Big Data Analytics play a significant role in enhancing detection accuracy, reporting efficiency, and cross-border trade transparency. However, the effective implementation of these technologies is heavily dependent on regulatory readiness, data quality, and human resource competencies. In Indonesia, despite a strong legal foundation and digital initiatives like CEISA 4.0, challenges persist in inter-agency integration and the refinement of technical regulations, such as the supervision of the Automatic Identification System (AIS). This study offers novelty by integrating a global literature mapping with an interview-based national readiness assessment, thereby providing a comprehensive perspective on the future direction for strengthening digital governance and technology adoption readiness within Indonesia's anti-TBML framework.]]>
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