Trade-based money laundering (TBML) is one of the most complex and elusive financial crimes, as sophisticated supply chains and increasing globalization create fertile ground for illicit transactions. Understanding how TBML works and leveraging advanced technology to prevent it are crucial to protecting institutions from financial, regulatory and reputational risks.
Financial crime detection empowered by artificial intelligence is emerging as an effective tool in overcoming the challenges of TBML. At the same time, however, the use of AI is a top concern for compliance and risk executives within the banking industry. In this blog, I’ll share some insights on the use and benefits of AI-driven financial crime detection as banks seek ways to combat TBML.
How TBML works
At its core, TBML exploits trade transactions to move illicit funds across borders. This is primarily done through invoice manipulation, such as the following:
- Over-invoicing and under-invoicing: Artificially inflating or deflating the value of goods and services to disguise financial transfers
- Multiple invoicing: Issuing multiple invoices for the same shipment to create fraudulent financial flows
- Misrepresentation of goods and services: Falsifying descriptions to obscure true values and avoid scrutiny
Additional TBML tactics include the use of Hawala networks, shell companies and offshore accounts, which add layers of complexity and hinder financial institutions' ability to track transactions effectively.
TBML red flags in banking
Financial institutions must be vigilant in detecting TBML, despite limited access to trade documents. Key TBML indicators include the following:
- Unusual payment patterns: Transactions inconsistent with a client’s profile or structured to avoid reporting thresholds
- Mismatched trade and financial profiles: Sudden spikes in trade activity without corresponding operational costs
- Shell companies and offshore accounts: Payments to newly established firms with no trade history or connections to tax havens
- Rapid fund movements: Quick deposits and withdrawals or international fund transfers without clear business justifications
- High-risk trade finance practices: Non-secured transactions, excessive third-party payments or reliance on cryptocurrency
Financial regulatory pressures
TBML enforcement is intensifying worldwide. The Financial Action Task Force (FATF), U.S. AML Act (2020), EU AML Directives, and Basel Committee Guidelines emphasize stringent due diligence, transparency and cross-border collaboration. However, inconsistent regulations and data privacy constraints remain obstacles, making proactive detection strategies essential.
AI and machine learning: Transforming TBML detection
Traditional compliance measures struggle to keep up with the sophistication of TBML schemes. Banks are under pressure to integrate AI-driven solutions to enhance risk management. AI and machine learning offer the following advantages:
- Real-time monitoring: Detecting anomalies in transaction flows as they occur
- Pattern recognition: Identifying suspicious behaviors by analyzing vast datasets
- Automated risk scoring: Enhancing decision-making with predictive analytics
- Seamless legacy system integration: Ensuring efficient compliance without disrupting existing processes
Finding a TBML mitigation partner
Banks can more effectively advance their TBML mitigation efforts with the expertise of a partner that delivers cutting-edge financial crime prevention solutions, including AI-powered solutions. The ideal solution is a modular detection platform that delivers the following capabilities:
- Global watchlist screening
- AI-driven transaction monitoring
- Advanced risk assessment tools
- Seamless integration via REST APIs
Strengthening your TBML defenses
As financial crime continues to evolve, leading banks are staying ahead with AI-powered compliance, cross-border intelligence sharing and advanced due diligence frameworks. At CGI, we have a team of experts who specialize in helping financial institutions navigate these challenges and implement state-of-the-art AML strategies, as well as advanced, AI-driven solutions such as CGI Hotscan360.
Contact me for a conversation on how your organization can enhance its TBML detection capabilities and safeguard against emerging threats.
About this author
Henry Kolowrat
Senior Business Consultant, Financial Services
Henry is a seasoned financial services professional with more than 30 years of experience working for private equity-financed small to medium enterprises (SMEs). His career is marked by versatility and insights-driven leadership, underpinned by a robust understanding of business operations, including auditing and accounting standards ...
