<ul><li>Financial institutions are facing increased pressure to maintain robust AML surveillance as real-time payments become more prevalent.</li><li>Traditional AML systems are inadequate for real-time payment environments due to latency issues, scalability limitations, and lack of contextual awareness.</li><li>To address these challenges, institutions are adopting event-driven architectures with real-time risk scoring engines, dynamic rule engines, and ML-driven anomaly detection.</li><li>Hybrid human-AI decisioning models are being used to improve detection precision and operational efficiency in AML systems integrated with real-time payment systems.</li></ul>

Integrating AML Surveillance with Real-Time Payment Systems at Scale

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