AML Model Risk Validation
This fast-paced and informative session will cover a number of trending AML Model Validation topics including lessons learned from NYDFS Part 504, how an AML review is seen from an auditor’s perspective and the significant impact Artificial Intelligence (AI) is having, and will continue to have, on AML.
- Demonstrate how to execute a Model Risk Audit for AML systems
- Describe the impact of Machine Learning on AML, today and tomorrow
- Summarize lessons learned from NYDFS Part 504
CPE Credit Offered: 1
Jim Sha is an Associate Director in the Model Risk Practice of Protiviti's Data Management and Advanced Analytics Solution. He has more than 10 years of experience in financial services technology, specializing in Anti-Money Laundering systems and analytics. Jim also has extensive data analytics experience using and validating various AML monitoring platforms and has deep business and technology experience with large global financial.
Avi Voruganti is a Model Risk Associate Director, responsible for risk consulting, including retail and wholesale credit risk model development and validation, CCAR, Basel II regulatory compliance. He has extensive experience in data analysis and credit risk modeling, including development and validation of PD and LGD models, application and behavior scorecards.
Greggy Samonte has conducted model risk and validation projects for multiple clients within the financial services industry. He has significant experience in data analysis and model risk management, focusing on credit scorecards, stress testing, and anti-money laundering.