Strengthening International Trade Finance Operations: Strategies for Preventing Trade-Based Money Laundering (TBML)

Abstract

This study examined the effectiveness of strategies for strengthening international trade finance operations in preventing trade-based money laundering (TBML) through a comprehensive quantitative approach. A longitudinal research design was employed using panel data from 48 countries over a ten-year period, resulting in 480 country-year observations. The analysis integrated trade finance indicators, institutional quality indices, and regulatory performance measures to assess the determinants of TBML risk. Descriptive findings indicated that the mean trade discrepancy was 12.85%, with high-risk sectors such as precious metals and electronics exhibiting discrepancy levels exceeding 18%, while low-risk sectors remained below 9%. The econometric results revealed that trade discrepancies had a strong positive effect on TBML risk (β = 0.412, p < 0.001), while institutional quality (β = -0.365, p < 0.001) and regulatory stringency (β = -0.284, p < 0.001) significantly reduced risk levels. The model explained approximately 61% of the variation in TBML indicators, demonstrating substantial explanatory power. Panel data analysis further confirmed that improvements in regulatory frameworks and financial monitoring systems were associated with consistent reductions in TBML risk over time, with the risk score declining from 0.49 in 2014 to 0.34 in 2023. Sectoral and geographic analyses revealed that developing economies exhibited higher TBML risk (mean = 0.52) compared to developed economies (mean = 0.34), with high-risk trade corridors reaching levels as high as 0.67. Advanced analytical techniques, including clustering and network analysis, identified concentrated transaction clusters associated with repeated counterparties and high-risk jurisdictions, indicating structured patterns of suspicious trade activity. The findings highlighted that TBML risk is influenced by a combination of trade complexity, institutional capacity, regulatory enforcement, and sector-specific characteristics. The study demonstrated that strengthened governance frameworks, enhanced compliance systems, and the integration of data-driven analytical tools significantly improve the detection and mitigation of TBML within global trade finance systems.