Read the Article Snippet: "Making Machine Learning Work for AML"
As money laundering fines grow and criminal typologies change, Financial Service Organizations (FSOs) are drafting in robots to help tackle false-positive rates as high as 95% - but how do we explain how these machine learning models are reaching their conclusions?
Access the free article snippet below for a summary of machine learning best practices and insights from:
- Ted Sausen, Director and AML Subject Matter Expert, NICE Actimize
- Evan Weitz, Managing Director and Regional Head of Controls, Standard Chartered Bank
- Jayati Chaudhury, Global Investment Banking Lead for AML Transaction Monitoring, Barclays
- Moderator: Duncan Wood, Editor-in-chief, Risk.net
