Live Webinar

Making Next-Gen AML a Reality With AI and Machine Learning

AI and machine learning have the ability to transform your AML programs, but only if you select a vendor with proven implementation capability. Join us for this webinar to learn how SAS expertly delivers next-gen AML.

April 30 • 10 a.m. ET • Cost: Complimentary

About the webinar

Financial institutions and regulators understand the immense value that AI and machine learning can bring to their AML programs.

Vendors everywhere promise to help firms combat money laundering with these advanced capabilities, but in actuality, only a handful of AML vendors have true AI and machine learning implementation expertise to make next-gen AML a reality for any organization.

SAS has a proven track record in helping financial institutions implement AI-based models to enhance detection accuracy, reduce false positives and significantly increase investigation efficiency, while lowering compliance costs.

In this webinar, you’ll hear from SAS experts as they share their AML implementation experiences, challenges and lessons learned.

Find out:

  • What your road map to next-gen AML should look like.
  • Points to consider before implementing AI models.
  • Key lessons learned from AI implementations for AML.

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About the Experts

Christopher Ghenne
Global Lead for Banking Compliance Solutions, SAS

Christopher Ghenne has more than 15 years of experience in combating anti-money laundering. As part of the Global Security and Intelligence Division in the SAS Brussels office, he leads a global team of AML subject-matter experts. He is responsible for all SAS compliance solutions and for training and supporting SAS pre-sales teams worldwide. He holds a master's degree in economics from the University of Brussels and has been accredited as a Certified Anti-Money Laundering Specialist (CAMS) since 2009.  

Beth Herron
Security Intelligence Practice, Americas AML Team Lead, SAS

Beth Herron has more than seven years of experience supporting the financial crimes space, specializing in anti-money laundering analytics, detection strategies and case management. She leads a team of subject-matter experts who support financial institutions with pre- and post-sales support. Prior to joining SAS, she worked within the financial intelligence unit of a top five global financial institution. Herron holds a master’s degree in industrial organizational psychology. She has been accredited as a Certified Anti-Money Laundering Specialist (CAMS) and currently serves on the board of the ACAMS Carolinas Chapter.

Amith Satheesh
Principal Solutions Architect for Fraud and Security Intelligence Solutions, SAS

Amith Satheesh is a financial crimes domain consultant who uses advanced analytics and industry best practices to optimize AML behavior monitoring and associate conduct risk. He was previously a director for a top US bank on the wholesale banking financial crimes analytics team, where he spearheaded the rollout of a front-line monitoring program.

Satheesh also worked at a top risk advisory firm helping clients in scenario development and optimization, AML model validation, false-positive reduction, AML program remediation, AML lookbacks, sanctions screening optimization, trade-based money laundering and brokerage surveillance. He is a Certified Anti-Money Laundering Specialist (CAMS) and has more than 19 years of experience in the financial services industry.

David Stewart
Business Director of Security Intelligence Solutions for Banking, SAS

David Stewart leads strategy development, drives product management and provides key marketing counsel for fraud and compliance solutions worldwide. In addition to working closely with many of the world’s leading financial institutions and regulatory agencies, Stewart collaborates with SAS R&D and delivery teams to deploy industry best practices for financial crimes solutions.

During the past 10 years, Stewart has hosted SAS’ annual Counterterrorism and Financial Crimes Forum co-sponsored by the US Department of Justice and a number of global financial institutions. He has collaborated extensively to bring data mining and machine learning capabilities to market for credit analytics, customer intelligence and financial crimes applications.