Case Study: Major Financial Institution improves AML detection and investigations with TigerGraph

Major Financial Institution Improves Its Ability to Combat Money Laundering with TigerGraph

Major Financial Institution, one of the largest banking organizations in the United States, needed to improve its anti‑money laundering (AML) networking and link analysis to: surface connections between open work items (e.g., prior SARs), support thorough ad‑hoc entity reviews, and help analysts determine which connections should drive creation, hibernation, or escalation of work items. To address these gaps, the institution engaged TigerGraph and its graph database capabilities (including GraphStudio/TigerGraph Cloud and graph ML tooling) to move beyond legacy relational analytics.

TigerGraph implemented an end‑to‑end AML solution that connects heterogeneous datasets (including feeds like NICE Actimize), extracts graph features, enriches the graph, and supports in‑graph or external ML models to score incoming transactions and trigger alerts via GraphStudio dashboards. After only months in production the Major Financial Institution reports improved ability to identify and trace connections in real time, greater effectiveness and efficiency for analysts and investigators, and substantial productivity gains from better prioritization—demonstrating TigerGraph’s scalability and impact on AML operations.