Anti-Money Laundering (AML) and Fighting Fraud in DataRobot
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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
- DataRobot Pathfinder User Case library—Reduce False Positives for Anti Money Laundering (AML)
- DataRobot.com on-demand webinar—Enhancing Anti-Money Laundering (AML) Programs with Automated Machine Learning
- DataRobot Platform Documentation:
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