Financial Crime Modernization is Stalling for a Simple Reason

Financial institutions are facing challenges in modernizing their anti-money laundering (AML) and know-your-customer (KYC) programs due to fragmented and flat data structures spread across various systems, leading to high reliance on manual processes for assembling and interpreting data. The inability to operationalize connected context across the full lifecycle hampers automated decision-making, as investigators must manually connect disparate data points to understand the relationships between entities, accounts, and transactions. This fragmentation results in increased costs and inefficiencies, despite investments in technologies like robotic process automation (RPA), which fail to achieve true end-to-end automation. A survey highlights that satisfaction with current AML/KYC technologies remains low, with many institutions planning further investments to address specific operational gaps. The proposed solution involves adopting a graph model, which offers a connected layer that improves context assembly, enhances detection logic, and supports network-aware scoring, with TigerGraph being an example of a platform that facilitates this approach by enabling connected entity views and explainable evidence paths across fragmented data sources.

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