Financial institutions typically classify AML/CFT vulnerabilities into product, service, transaction, customer, channel, and geographical categories in line with leading international guidance (e.g., FATF, EBA). In AMLTRIX, we slightly adjust this approach by consolidating products, services, and transaction activities into a single Product Risk category, reflecting the natural overlap in real-world usage. We also highlight Internal Risk to give explicit focus to operational and governance-related threats—though many frameworks would consider these under “controls” or “governance.”
Below is a concise explanation of each risk type, with an emphasis on how these categories can help map and label specific laundering techniques in everyday AML/CFT operations.
1. Product Risk
(Combines references to products, services, and transactions)
Scope
Financial offerings or transaction types that, by design or usage, can facilitate money laundering—particularly those providing anonymity, layering flexibility, or rapid cross-border flows.
Relevant Factors for Tagging Behaviors
- Anonymity-Focused Features
- Prepaid cards with high load limits
- Cryptocurrency services with minimal KYC
- Complex or High-Volume Structuring
- Trade finance instruments, specialized derivatives
- Rapid International Transfers
- Channels allowing large, quick cross-border remittances with limited checks
Behavioral Examples
- Splitting a large deposit into many small amounts via prepaid cards
- Layering funds across multiple jurisdictions using high-speed international remittance
2. Customer Risk
Scope
Aspects of a client’s profile—ownership structures, business models, or behaviors—that heighten ML or TF threats.
Relevant Factors for Tagging Behaviors
- Opaque or Complex Ownership
- Shell companies, offshore vehicles, nominee directors
- High-Profile or Potentially Corrupt
- PEPs, high-cash sectors like casinos, used-car dealerships
- Unusual Patterns
- Reluctance to provide standard documentation, suspiciously large or swift transactions
Behavioral Examples
- Rapid changes in beneficial ownership in corporate structures
- Cash activity not aligning with the stated nature of the client’s business
3. Channel Risk
Scope
Potential vulnerabilities tied to how products/services are delivered or accessed—particularly remote or heavily intermediated channels.
Relevant Factors for Tagging Behaviors
- Non-Face-to-Face Interactions
- Online-only account openings with minimal identity verification
- Multiple Intermediaries
- Complex correspondent banking chains, unregulated local agents
- Emerging and Unregulated FinTech
- Payment apps or crypto exchanges lacking oversight
Behavioral Examples
- Layering funds via digital wallets or e-payment gateways
- Creating accounts remotely with insufficient ID checks
4. Geographical Risk
Scope
Jurisdictional or regional factors that expose an institution to higher laundering risks, such as sanctioned territories or corruption hotspots.
Relevant Factors for Tagging Behaviors
- Sanctioned or Conflict Zones
- Embargoed countries, war-torn regions
- Weak Regulatory Environments
- Jurisdictions with limited AML controls, elevated organized crime
- Secrecy & Offshore Havens
- Areas with strict banking secrecy or limited disclosure
- Regulatory Misalignment
- Differences in AML/CFT standards, implementation, or enforcement—even in jurisdictions with generally well-developed legal systems—that can lead to regulatory arbitrage
Behavioral Examples
- Structuring fund transfers through countries with lighter AML reporting obligations to reduce scrutiny
- Using legal entities in secrecy jurisdictions to disguise beneficial ownership
- Structuring shipment routes through sanctioned territories to hide final recipients
- Leveraging mismatched regulatory regimes to shift assets with limited visibility or delay compliance actions
5. Internal Risk
Scope
Institutional and governance shortcomings—often recognized under “internal controls”—that enable or fail to detect illicit conduct within the bank or firm.
Relevant Factors for Tagging Behaviors
- Weak Governance & Culture
- Insufficient board engagement, under-resourced compliance function
- Insufficient Training & Oversight
- Staff unaware of key red flags, minimal internal auditing
- Collusion or Conflicts of Interest
- Senior management or employees enabling suspicious transactions
- Legacy or Fragmented Systems
- Outdated monitoring platforms, siloed data hindering enterprise-wide detection
Behavioral Examples
- Failing to escalate suspicious transactions due to staff complicity
- Neglecting AML red flags because employees lack adequate training
Linking Techniques to Risk Categories
Each technique in AMLTRIX is cross-referenced with one or more of the five risk categories (Product, Customer, Channel, Geography, Internal) via an explicit relationship in the knowledge graph. This alignment clarifies where and why a given tactic or sub-technique might exploit a particular vulnerability in an institution. For instance, a laundering method that relies on minimal face-to-face interaction might be mapped to Channel Risk, while a structuring strategy leveraging complex derivative instruments would likely be linked to Product Risk.
By capturing these connections at the technique level, AMLTRIX enables practitioners to:
- Pinpoint Vulnerabilities: Understand exactly which risk dimensions (e.g., product, customer profile) a laundering method targets.
- Improve Detection Rules: Tailor monitoring logic to specific vulnerabilities, ensuring tighter alignment between detection policies and known adversarial tactics.
- Enrich Analytics and Reporting: Query which techniques predominantly exploit certain risk types, refining threat assessments and resource allocation.
- Enhance Regulatory Oversight: Demonstrate clear, evidence-based mappings between everyday compliance processes and the underlying AML/CFT risk framework.
In essence, correlating techniques with risk categories bridges the gap between high-level vulnerabilities and detailed illicit methods—deepening both strategic risk management and operational detection.