Case Study: Streamlining Trade Surveillance with Machine Learning

How can machine learning tools revolutionise trade surveillance by making the analysis process quicker, more cost-effective, and efficient? By training on historical data, these tools learn to distinguish between relevant and non-relevant alerts, providing analysts with prioritized evaluations and relevance scores. This helps focus on critical alerts and also automatically close low-relevance ones, optimising the overall surveillance workflow.