A money laundering typology is a formal classification framework that systematically categorizes the distinct methods, mechanisms, and procedural patterns used to conceal the origin of criminal funds. Developed by organizations like the Financial Action Task Force (FATF) and national FIUs, typologies move beyond simple stage-based models—placement, layering, integration—to document specific, observed schemes such as trade-based laundering, shell corporation cascading, or virtual asset layering. Each typology entry typically includes the scheme's mechanics, red-flag indicators, and the vulnerabilities exploited, enabling financial institutions to update their transaction monitoring rulesets and behavioral profiling models against known criminal innovation.
Glossary
Money Laundering Typology
What is Money Laundering Typology?
A money laundering typology is a structured classification system used by financial intelligence units (FIUs) to categorize, document, and share the evolving methods, techniques, and schemes criminals use to disguise illicit proceeds.
Typologies serve as a shared intelligence language across jurisdictions, allowing analysts to connect seemingly disparate suspicious activities into coherent criminal methodologies. In modern anti-money laundering systems, these classifications are operationalized as feature engineering templates: a typology describing structured cash deposits just below reporting thresholds directly informs structuring detection algorithms, while a typology on invoice manipulation feeds trade-based money laundering (TBML) models. By continuously updating typology libraries with emerging schemes—such as those exploiting decentralized finance bridges or non-fungible token wash trading—financial institutions maintain adaptive defenses against an evolving threat landscape.
Frequently Asked Questions
Clear, technical answers to the most common questions about how financial criminals structure illicit transactions and how typologies are classified by financial intelligence units.
A money laundering typology is a standardized classification framework used by financial intelligence units (FIUs) and compliance departments to categorize, document, and share the specific methods, techniques, and schemes criminals employ to disguise the illicit origin of funds. Typologies serve as a shared taxonomy that enables institutions to pattern-match suspicious activity against known criminal behaviors. They are not static legal definitions but evolving operational models—derived from case investigations, SAR filings, and cross-border intelligence sharing through bodies like the Financial Action Task Force (FATF) and the Egmont Group. In practice, typologies feed into transaction monitoring rule engines and machine learning feature engineering, allowing systems to detect structural signatures such as rapid layering through shell corporations, trade-based value transfer, or structuring patterns designed to evade currency transaction reporting thresholds.
Core Characteristics of AML Typologies
Money laundering typologies are structured classification systems used by financial intelligence units to categorize, analyze, and disseminate emerging criminal methodologies. They enable institutions to map specific techniques to detection rules and risk indicators.
Placement, Layering, Integration
The foundational three-stage model describing the money laundering lifecycle. Placement introduces illicit cash into the financial system through deposits or purchases. Layering separates proceeds from their source via complex transactions, shell corporations, and wire transfers across jurisdictions. Integration reintroduces laundered funds as apparently legitimate wealth through real estate, luxury assets, or business investments. Each stage presents distinct detection opportunities for anomaly models.
Trade-Based Money Laundering
A sophisticated typology exploiting international trade transactions to transfer value across borders. Key techniques include:
- Over/under-invoicing: Misrepresenting goods' value on invoices
- Phantom shipments: Documenting non-existent goods movement
- Multiple invoicing: Reusing the same shipment documentation repeatedly
- Short/over-shipment: Discrepancies between declared and actual quantities Detection requires cross-referencing customs data, shipping manifests, and payment flows.
Shell and Front Company Structures
Criminals establish legal entities with no genuine business activity to obscure beneficial ownership and create transactional distance from illicit funds. Shell corporations exist primarily on paper in secrecy jurisdictions. Front companies maintain a facade of legitimate operations to commingle dirty money with clean revenue. Entity resolution algorithms must pierce these structures by analyzing shared addresses, directors, and transactional patterns to reveal the controlling natural person.
Smurfing and Structuring
Structuring involves deliberately breaking large cash sums into smaller transactions below regulatory reporting thresholds. Smurfing distributes this activity across multiple individuals to further evade detection. Common patterns include:
- Deposits just under $10,000 at multiple branches
- Coordinated cash purchases of monetary instruments
- Rapid sequential transactions across accounts Velocity checks and aggregation algorithms are essential countermeasures, consolidating related transactions to identify the true cumulative value.
Virtual Asset Layering Schemes
Cryptocurrency and virtual assets enable novel typologies including chain-hopping (rapidly converting between different cryptocurrencies), mixing/tumbling services that pool and redistribute funds to break traceability, and privacy coin conversion to Monero or Zcash. Peel chains involve sending precise amounts to fresh wallets while returning change to a controlled address. Blockchain analytics tools apply clustering heuristics and attribution algorithms to map these flows despite pseudonymity.
Professional Enabler Networks
Sophisticated laundering operations increasingly rely on professional enablers—lawyers, accountants, trust formation agents, and company service providers who exploit their expertise and privilege to construct opaque structures. Typologies include:
- Trust and foundation abuse across multiple jurisdictions
- Escrow account manipulation to hold and move funds
- Legal professional privilege claims to obstruct investigations Detection focuses on anomalous professional service fees and circular transaction patterns between client accounts and professional firms.
Typology vs. Scenario vs. Red Flag
Distinguishing the structural, narrative, and indicator layers of money laundering intelligence to ensure precise categorization and effective detection.
| Feature | Typology | Scenario | Red Flag |
|---|---|---|---|
Definition | A classification of a distinct money laundering method or technique based on common characteristics. | A specific, narrative-driven instance of a typology applied to a particular sector, product, or geography. | An observable anomalous behavior or transaction indicator that may signal illicit activity. |
Level of Abstraction | High (Conceptual Model) | Medium (Contextualized Narrative) | Low (Specific Indicator) |
Primary Function | Categorizes and structures knowledge for systemic analysis and rule generation. | Illustrates how a typology manifests in a real-world operational context for training and risk assessment. | Triggers an alert or suspicion within a transaction monitoring system or manual review. |
Regulatory Usage | Used by Financial Intelligence Units (FIUs) like FATF and Egmont Group to share strategic intelligence. | Used by compliance teams to conduct sectoral risk assessments and design controls for specific products. | Used by analysts to justify Suspicious Activity Report (SAR) narratives and by systems to generate alerts. |
Static or Dynamic | Relatively static; evolves slowly as criminal methodologies shift. | Dynamic; updated frequently to reflect changing geopolitical contexts and new product vulnerabilities. | Highly dynamic; continuously tuned to balance detection rates against false positive ratios. |
Example | Trade-Based Money Laundering (TBML) via over/under-invoicing of goods. | A shell company in Jurisdiction A over-invoices a precious metals importer in Jurisdiction B to move value across borders. | The declared value of a shipment on a customs invoice deviates by more than 25% from the estimated fair market price. |
Machine Learning Mapping | High-level class label for supervised model training. | Synthetic data generation template for simulating complex, multi-step sequences. | Feature input or rule trigger for real-time scoring engines and anomaly detection algorithms. |
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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