Subscribe to DSC Newsletter
Webinar Details
Thursday, January 11, 2018 Speakers: Justin Dickerson, GM of
1:00 pm Eastern / 10:00 am Pacific Global Finance, DataRobot; Dan Yelle
45 minutes with Q&A Customer-Facing Data Scientist, DataRobot
Dear DSC Member,
Compliance organizations are turning to machine learning for improving their Anti-Money Laundering (AML) 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, Justin Dickerson, General Manager of Global Finance for DataRobot, and Dan Yelle, 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 fraud detection predictive models at any time
  • The ability to deploy models with a click of a button
  • The ability to operationalize anti-money laundering models by following a process that is user-centric
Speakers:
Justin Dickerson,
GM of Global Finance, DataRobot
Dan Yelle,
Customer-Facing Data Scientist, DataRobot
Space is limited!
DataRobot on Twitter DataRobot on Facebook DataRobot on Google+ DataRobot on LinkedIn
DataRobot, Inc | One International Place Boston MA 02110

Views: 204

Follow Us

Videos

  • Add Videos
  • View All

Resources

© 2018   Data Science Central™   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service