Transaction Monitoring vs. Trade-Based Money Laundering

This article examines the effectiveness of traditional transaction monitoring systems in detecting trade-based money laundering (TBML), one of the most complex and under-detected forms of financial crime. While transaction monitoring has advanced through machine learning and analytics, most systems remain focused on financial transaction flows and lack visibility into the trade activity behind them.

Using a mixed-method research approach involving analysis of suspicious activity reports (SARs), trade finance transaction datasets, and interviews with AML and trade finance professionals, the study identifies significant limitations in conventional transaction monitoring frameworks. Findings show that traditional systems missed nearly 70% of TBML-related anomalies due to fragmented data systems, lack of trade metadata, and limited cross-domain visibility.

The paper proposes a practical three-pillar TBML Attestation Framework consisting of:

1. Enhanced TBML-focused Transaction Monitoring systems
2. Trade Anomaly Detection Systems (TADS)
3. Centralized Domain Cross-Correlation

The study recommends stronger regulatory coordination, cross-border data sharing, and improved public-private partnerships to enhance TBML detection and strengthen global AML compliance frameworks.