Network Based AML

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Network-based AML detects money laundering schemes involving multiple interconnected parties by analyzing relationship networks and transaction patterns. This use case addresses identifying hidden connections between parties, detecting complex network structures, understanding how money laundering networks evolve over time, distinguishing legitimate business networks from criminal networks, and leveraging KYC relationship data to identify suspicious patterns across customer networks.

The Challenge

Financial institutions struggle to detect network-based money laundering:

• Complex schemes — Money laundering often involves networks of individuals and organizations working together
• Fragmented view — Traditional systems analyze transactions individually, missing the network context
• Hidden connections — Relationships between parties may not be immediately apparent from transaction data
• Scale and complexity — Large networks with thousands of nodes and connections are difficult to analyze
• Pattern evolution — Criminals adapt their methods, requiring systems that can detect new network patterns
• False positive management — Distinguishing legitimate business networks from money laundering networks
• Regulatory requirements — Must demonstrate ability to detect network-based money laundering

Closely linked with the KYC use case and further discussed in the use case "High net-worth individuals" (HNWI), network-based AML requires understanding customer relationships and networks to identify suspicious patterns.

Why EKG is Required

Enterprise Knowledge Graphs provide powerful network analysis capabilities:

• Natural network representation — Graph structure inherently represents relationships and connections
• Network pattern detection — Use graph algorithms to identify suspicious network structures and patterns
• Multi-hop analysis — Traverse networks to find indirect connections and relationships
• Community detection — Automatically identify clusters and communities within customer networks
• KYC-AML integration — Knowledge Graph technologies enable powerful synergies between AML and KYC to identify patterns and networks that may be involved in illegal activity
• Temporal network analysis — Track how networks evolve over time and detect emerging patterns
• Holistic view — Connect all customer relationships, transactions, and entities in one unified view

Business Value

• Enhanced detection — Identify sophisticated money laundering schemes that span multiple parties
• Network insights — Understand the full structure of money laundering networks
• Proactive monitoring — Detect emerging network patterns before they become major issues
• Regulatory compliance — Demonstrate ability to detect network-based money laundering
• Operational efficiency — Automatically identify suspicious networks for investigation
• Risk mitigation — Prevent money laundering through early detection of network patterns

Related Topics

See also High Net Worth Individuals for discussion of network analysis in HNWI monitoring, where most large-scale money laundering tends to involve networks of individuals and organizations with a controlling force.