Neo4j enables large banks to process millions of transactions daily while resolving entities and flagging suspicious activity in real-time, improving the accuracy and efficiency of anti-money laundering (AML) compliance. The graph database stores complex relationships between parties, institutions, behaviors, attributes, and times, allowing for more effective entity resolution and scoring. Neo4j's AML Framework reduces false positives and negatives, automates and instills rigor to the ER-scoring process, and cultivates domain expertise to model relationships. Graph algorithms help fill in gaps of missing and inaccurate data, identify relevant subgraphs, and combine normalized scores to produce an overall similarity score. The framework also enables investigators to visualize suspicious clusters and entities at a high level, providing more relevant facts and circumstances surrounding flagged transactions. By leveraging Neo4j's capabilities, compliance teams can better comply with AML regulations, make more accurate predictions, save money on regulatory fines, increase sales by improving brand reputation, reduce costs associated with false positives and false negatives, and meet stringent requirements for performance, availability, security, and agility at extreme scale.