
Learn the basics of tbml, its methods, red flags and risk indicators, with case studies and ai-driven controls to counter money laundering.
Understand why trade-based money laundering matters, given global losses and compliance costs, and learn how TBML knowledge helps professionals, traders, and developers avoid litigation.
Understand international trade operations, the roles of banks and facilitators, and how documents and payment methods like letters of credit enable money laundering.
Explore how trade based money laundering differs from other methods, and how globalization, cross-border trade, and currency conversion enable TBML, highlighting regulation, monitoring, and risk indicators.
Explore how TBML definitions vary across FATF, Wolfsberg, Egmont, APG, and the UN, from narrow to broad. Trace 2006 and 2008 FATF shifts that include terrorist financing and capital flight.
Identify service-based money laundering and TBML types, including TBTF, sanctions evasion, tax crimes, and illegal wildlife trade. Learn how predicate offenses generate money and how it moves through international trade.
Explore the three stages of money laundering in TBML: placement, layering, and integration, including smurfing, fake invoices, shell companies, and cross-border transfers that obscure illicit funds.
Explore how trade based money laundering uses over and under invoicing and shipments, phantom shipments, and colluding partners to move and legitimize illicit funds across borders.
Explore how multiple invoicing of goods enables money laundering through different banks to dodge detection, while changes in payment terms or modes justify invoices at fair market value.
Identify how falsely described goods in invoices move value illegally by misdeclaring type and price. For example, a company declares gold plated screws as silver screws at a lower price.
Explore the black market peso exchange, a trade-based money laundering cycle where drug cartels convert dollars to pesos, layer funds through invoiced imports, and repatriate proceeds via peso brokers.
Understand the Financial Action Task Force (FATF) as the global watchdog for money laundering and terrorist financing, its 40 recommendations, risk-based approach, and the grey list and blacklist.
The Wolfsberg Group, a non-governmental association of 12 global banks founded in Basel, Switzerland, develops frameworks and guidance for anti-money laundering, know-your-customer, and counter-terrorism financing policies, mirroring FATF standards.
The Egmont Group links financial intelligence units to securely exchange expertise and combat money laundering and terrorist financing; FIUs collect and analyze suspicious activity and share findings with law enforcement.
Explore the Asia/Pacific group on money laundering, founded in 1997, guiding jurisdictions to implement international standards against money laundering, terrorist financing, and proliferation financing related to weapons of mass destruction.
Explore how the global coalition GCFFC unites public and private partners to fight financial crime by raising awareness, improving information sharing, and proposing AML/CTF best practices and solutions.
Identify red flags and jurisdiction-related risk indicators in TBML transactions, including high-risk country shipments, circuitous routes, unusual trade patterns, and vulnerable free trade zones.
Identify red flags in traded goods, from mis-invoicing and price discrepancies to HS code misclassification, dual-use items, and low value high-volume or high value low-volume risks.
Analyze how criminals use front and shell companies and related party transactions to enable TBML, and identify red flags like complex structures, dubious addresses, and unusual financial patterns.
Spot red flags in transactional activity, including letters of credit used to disguise money laundering. Note unusual payment patterns like last-minute amendments, high-risk suppliers, third-party involvement, and offshore cash flows.
Learn to spot red flags in trade documents, such as invoice and bill of lading discrepancies, minimal submissions, resubmissions with changes, and switch bills of lading to disguise sanctioned entities.
Explore how co-mingling legitimate food product sale proceeds with illicit blood diamond funds enables laundering through offshore accounts and rapid cross-border transfers.
Analyze how over-invoicing between a German supplier, a Norwegian firm, and its Balkan affiliate enables TBML by disguising criminal proceeds through inflated invoices and cash deposits.
Trace how underground banks inside the Israel Diamond Exchange move illegal cash through diamond companies, mix it with legitimate trading proceeds, and use fraudulent customs documentation to justify multi-million transfers.
Trace how gold trading fuels TBML by moving criminally derived gold from Latin America and the Caribbean through a Florida-based dealer to US refineries, with proceeds laundered as wholesale purchases.
Explore tbml through recent cases, including sez-based schemes with fake diamond declarations and the black market peso exchange, illustrating high-value goods and regulatory gaps.
Explore how banks assess trade-based money laundering risk using the Wolfsberg three-phase framework: inherent risk, internal controls, and residual risk, shaped by client, product, governance, training, and risk appetite.
Explore how banks deploy KYC to verify identities and deter fraud, apply ongoing CDD to monitor risk, and implement sanctions screening to halt transactions and file SARs against TBML.
Identify red flags in letter of credit transactions, such as unusual goods or related parties, and apply KYC, CDD, and AML controls to detect TBML.
Use ai and ml to analyze transaction data, detect anomalies, and prioritize money-laundering alerts. Improve name screening, login anomaly detection, and network analysis to reveal beneficial owners and end users.
Compare rule-based and machine learning approaches for aml compliance, noting predefined thresholds and easy explainability in rules, and how supervised and unsupervised learning in ml reduces false positives.
Explore ai/ml applications in aml compliance, featuring United Overseas Bank's Anti Money Laundering Suite with Know Your Customer, transaction monitoring, name screening, and payments screening.
The success of AI in combating tbml hinges on data quality, algorithm integrity, and explainability, with regulatory governance, security, and cost considerations shaping adoption.
Access helpful resources and reference websites to deepen your understanding of trade finance and international trade; enroll in recommended courses and use the Q&A for questions.
A 360 degree overview of Trade Based Money Laundering (TBML) covering all the basic concepts
50+ Multiple Choice Questions
Latest Update 2025 - 1. Use of AI/ ML in combating Money Laundering
2. Current news related to AML (2025)
Trade Based Money Laundering (TBML) is a vast and ever-changing subject. It has been a massive pain point for regulatory bodies and financial institutions since decades. As a trade finance practitioner, or a international trader it is an absolute necessity to have a basic knowledge about trade based money laundering.
This course provides you an overview about TBML in a no-nonsense, compact and easy-to-understand way.
This course starts from the very basics, goes through all the essential knowledge of TBML techniques and typologies, red flags and risk indicators and control mechanisms. It also covers how artificial intelligence and machine learning (AI/ML) is changing the way trade based money laundering is countered.
With quizzes, practice test combined with English captioning, you are well equipped to up-skill yourself most effectively when you enroll in this course.
Ideal for:
Trade Finance Practitioners
Business Analysts and Consultants of Trade Finance
Entrepreneurs and Small Business Owners Engaged in Global Trade
What this course covers:
Introduction to Trade Based Money Laundering
Trade Based Money Laundering (TBML) Vs Trade Based Financial Crime (TBFC)
Trade Based Money Laundering Techniques and Typologies
Governing/ Contributing bodies related to Trade Based Money laundering and Financial Crimes
TBML Red flags/ Risk Indicators
TBML Case Studies
Control Mechanisms to counter TBML
Use of AI/ ML in combating Money Laundering
Resources
Practice Test