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Employing Machine Learning to Transaction Monitoring is proven, with the appropriate governance and expertise, to achieve higher rates of suspicious activity identification and improve efficiency.

Machine Learning advanced analytics tools such as feature importance, random forest, deep learning, neural networks, natural language processing and gradient boosting are best practices to support Transaction Monitoring.  

Research published by leading consulting firms, government agencies and forward-thinking-progressive banks with Financial Crime Anti-Money Laundering (AML) teams have documented improved efficiency up to 30% and identification of suspicious activity up to 40%. In addition, the benefits of such models can reduce false-negatives and false-positive results.