Network-based detection is an advanced method of identifying suspicious behaviour by analysing the relationships and interactions between entities, rather than evaluating them in isolation. In the context of anti-money laundering (AML) compliance, this approach helps institutions uncover complex schemes such as money laundering networks, sanctions evasion structures, or collusive fraud patterns that traditional transaction monitoring may overlook.
As financial crime grows in sophistication, compliance frameworks must evolve. Network-based detection provides deeper insights by mapping links between customers, accounts, and transactions to reveal connections that may indicate illicit activity.
Network-Based Detection
Network-based detection refers to analytical techniques that use graphs, link analysis, and relational data models to detect suspicious activities within interconnected systems. Instead of monitoring single transactions, it focuses on how entities are linked, through shared addresses, repeated transaction flows, or common counterparties.
This method is increasingly adopted in AML because criminal networks often use multiple intermediaries or layered structures to disguise the origin of funds. Research from Network Analytics for Anti-Money Laundering shows that graph-based models and GNNs applied to transactional networks (e.g. blockchain or banking datasets) can substantially increase detection of hidden structures and abnormal connections in financial systems.
How Network-Based Detection Works
Network-based detection works by creating a visual or mathematical model of the relationships within financial data.
This often involves:
Graph Modelling - representing customers, accounts, and transactions as nodes and edges in a network.
Pattern Recognition - identifying unusual clusters, transaction loops, or high-risk hubs.
Entity Resolution - ensuring that linked entities are correctly identified across multiple datasets.
Anomaly Detection - spotting deviations from normal network patterns that may signal illicit activity.
Financial institutions integrate network-based detection with tools such as Transaction Monitoring (FacctGuard) and Alert Adjudication to ensure that suspicious links are flagged and investigated appropriately.
Network-Based Detection In AML Compliance
Network-based detection has become a vital tool for AML compliance teams. Traditional rule-based transaction monitoring may flag individual alerts but miss systemic risks across multiple accounts.
By contrast, a network view enables institutions to:
Detect money laundering rings operating across different banks.
Identify relationships between sanctioned individuals and otherwise low-risk customers.
Expose shell company structures and nested correspondent banking activity.
The Financial Action Task Force (FATF) has emphasised the importance of technological innovation, including network analytics, to strengthen AML measures. For example in its “Outcomes FATF Plenary, 20-25 June 2021,” where it reported on advances in using advanced analytics and machine learning to detect suspicious ML/TF behaviour.
Network-based detection is also increasingly used in Customer Screening (FacctView) to uncover indirect associations with sanctioned or high-risk entities.
The Future Of Network-Based Detection
The future of network-based detection lies in its combination with artificial intelligence and big data. As financial transactions become faster and more globalised, static monitoring rules are insufficient to capture complex patterns of abuse.
Emerging approaches use machine learning layered over network analysis to predict potential risks and identify hidden relationships in real-time. Regulators such as the Bank for International Settlements (BIS) are calling for deeper data integration and advanced analytics to combat evolving financial crime threats.
For example, in Project Hertha, which shows how payment system analytics and AI techniques can uncover previously unseen illicit behaviour in transaction networks.
As these technologies mature, compliance teams will increasingly rely on network-based detection to stay ahead of sophisticated laundering and sanctions evasion schemes.
Strengthen Your Network-Based Detection Compliance Framework
Financial institutions that invest in network-based detection can uncover risks that traditional systems overlook. By combining advanced analytics with robust monitoring and screening, organisations can build stronger, future-ready compliance frameworks.
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