Detecting Trade-Based Money Laundering: A Literature Review of Red-Flag Frameworks and Network-Analytic Detection Methods
Abstract: Trade-based money laundering (TBML) is widely regarded as one of the hardest forms of money laundering to detect, because it hides inside ordinary import-export documentation rather than a suspicious financial transfer. Regulators have responded largely with red-flag indicator lists, while separate and much larger literatures have applied network analysis, machine learning and customs-fraud analytics to adjacent detection problems. This paper reviews these strands to establish what is known about detecting TBML and where the evidence base is thin. Thirty-five academic papers were identified through seven structured searches of an academic database, supplemented by four regulatory and institutional publications, and synthesised thematically. The review finds that regulatory red-flag taxonomies are detailed and well established but designed for manual, document-level review; that TBML-specific peer-reviewed research remains genuinely scarce, a finding independently confirmed by three prior systematic and bibliometric reviews of the field; and that a large, mature and well-validated customs-fraud-detection literature exists on directly adjacent problems, such as under-invoicing and misclassification, yet is rarely cross-referenced by TBML-specific research. The general anti-money-laundering network-analysis and machine-learning literature reports strong detection performance but is drawn almost entirely from financial transaction or cryptocurrency data rather than trade documentation. The review closes by setting out this gap, a research agenda for closing it, and the methodological limitations of the search process used here.
Keywords: Trade-based money laundering, Literature review, Red flags, Network analysis, Customs fraud detection
How to Cite?: Malki Senevirathne, "Detecting Trade-Based Money Laundering: A Literature Review of Red-Flag Frameworks and Network-Analytic Detection Methods", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1426-1431, https://www.ijsr.net/getabstract.php?paperid=SR26921222823, DOI: https://dx.doi.org/10.21275/SR26921222823