Fraud/Anti-Money Laundering (AML)

Community Team
Community Team
4 1 656

(Part of a model building learning session series.)

A key component of any financial crime compliance program is to monitor transactions for suspicious activity. Typically the systems that aim to detect potentially suspicious activity are rule-based and suffer from ultra-high false positive rates. Automated machine learning provides a solution to address this challenge by dynamically learning patterns in complex data and significantly improving model accuracy in predicting which cases will result in suspicious activity reports.

In this session Ray Mi (DataRobot Customer-Facing Data Scientist) will show how banks can implement automated machine learning to improve investigation efficiency, reduce false positive alerts, and lower operational costs.

Hosts

  • Ray Mi (DataRobot, Data Scientist)
  • Rajiv Shah (DataRobot, Data Scientist)
  • Jack Jablonski (DataRobot, AI Success Manager)

More information

Let us know what you think!

Have questions not answered during the learning session? Want to continue your conversation with Ray and Rajiv (@rshah)? Post your Comment here, or send email to learning_sessions@datarobot.com. We look forward to hearing from you!

1 Comment
Data Scientist
Data Scientist

This is such a great learning session!

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