AML with Graph Analytics: Detecting Money Laundering Patterns That Hide in Plain Sight
Blog post from TigerGraph
Money laundering, which accounts for 2–5% of global GDP annually, often eludes detection by traditional Anti-Money Laundering (AML) systems that assess transactions in isolation, ignoring the interconnected networks crucial for identifying sophisticated schemes. Graph analytics offers an innovative approach by analyzing the network of accounts, entities, and transactions, making it possible to detect patterns such as smurfing, circular flows, and shell company layering that traditional systems often miss. This method enhances AI-based AML by incorporating relationship-aware features, improving the accuracy of risk models over those trained on transaction data alone. TigerGraph exemplifies the application of this technology, providing real-time deep-link pattern detection and entity resolution, which enables compliance teams to reduce false positives, strengthen Know Your Customer (KYC) processes, and generate explainable risk scores critical for regulatory reviews. By shifting the focus from isolated transactions to the broader financial network, graph analytics allows for more effective detection of laundering activities, thereby addressing the significant gap in legacy AML systems' ability to uncover hidden money laundering patterns.
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