Glance at Network - Based Detection
Network-based detection means analysing interactions and relationships to identify suspicious behaviours between entities. In this method, an individual isn’t evaluated in isolation, but studied collectively to identify links between accounts, devices, customers, their IPs, transactions, and wallets.
AML network analysis uses graphs, relational data, and link analysis to find out hidden criminal activities such as mule rings, shell companies, and fraud across different platforms.
The traditional transaction monitoring system focuses on individuals to detect suspicious activities, reviewing accounts one at a time and constructing a network. In contrast, multi-entity network analytics provides a holistic view by analysing the connections between various entities. Multi-entity analysis further helps in detecting complex AML/CFT typologies such as layering and funnel accounts.
AML network analytics uses the following techniques and patterns to detect criminal activities:
Regulated entities must identify the following risks and red flags:FATF emphasised leveraging new technologies such as network analytics to strengthen AML/CFT measures.
It expects regulated entities to detect complex criminal networks and ensure transparency of beneficial ownership.
Further, FinCEN, EU AMLR, and FCA also expect the application of risk-based customer due diligence to monitor and assess ML/FT risks.
Moreover, regulators require entities to use the results of risk assessments and develop mitigation plans.
Regulated entities must also document how network-based AML models work, how they generate risk scores or alerts, and how they conduct ongoing reviews of the system, ensuring transparency, control, & accountability.
Entities are also required to report SAR/STR for suspicious activities detected and must provide detailed narratives for network-based monitoring.
In addition, regulators expect entities to regularly check network-based model accuracy, adjust thresholds to never miss suspicious activity and ensure data control & security.
RapidAML provides a clear picture of information by automatically identifying and connecting individual or entity information across various data sources.
The software uses a graph-based detection to identify hidden connections and unusual group networks, which may indicate money mules or layering.
The transaction monitoring software with advanced AI-driven behavioural baselining helps identify a customer’s normal behaviour and compare it to similar peers to detect unusual activities.
RapidAML Software automatically generates alerts for mule rings, network anomalies and suspicious fund flows, enabling real-time detection & prevention of ML/FT risks.
The software, with its effective case management, provides a single interface for managing workflows, while visualising connections using network graphs.
Network-based detection uses machine learning and behavioural analytics to find connections or patterns across entities, while traditional rule-based monitoring focuses on a single transaction or individual to detect anomalies.
AML network analytics helps identify money mules, layering, smurfing, shell companies, sanctions evasion, and TBML.
Investigators represent customers, transactions, and accounts as edges and nodes in a network and use tools such as transaction monitoring to flag suspicious links and investigate properly.
Yes, network-based detection helps reduce false positives as it checks connections and flags unusual transactions, rather than traditional AML monitoring, which generates unnecessary alerts based on a single transaction.
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