Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. Automated machine learning provides a solution to address this challenge.
In this webinar, Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.
You’ll discover how Automated Machine Learning provides:
- The ability to develop and refresh AML predictive models at any time
- The ability to deploy models with a click of a button
- The ability to operationalize AML models by following a process that is user-centric