Sam Rezaei
About Sam Rezaei
Sam Rezaei is a Quantitative Credit Risk Manager at City National Bank, where he has worked since 2015. He holds a Bachelor's degree in Electrical Engineering from Sharif University of Technology and two Master's degrees in Finance and International Business from The George Washington University and Hult International Business School, respectively.
Work at City National Bank
Sam Rezaei has been serving as a Quantitative Credit Risk Manager at City National Bank since 2015. In this role, he has been involved in various critical projects, including leading the modeling implementation for Allowance for Loans and Lease Losses (ALLL). He developed a risk rating transition probability model based on macroeconomic variables for Comprehensive Capital Analysis and Review (CCAR) stress testing. Additionally, he created a portfolio aggregation process for the 2016 CCAR mock stress testing using VBA. Prior to his current position, he worked as a Portfolio/Data Analyst at City National Bank for four months in 2015.
Education and Expertise
Sam Rezaei holds a Bachelor of Science in Electrical Engineering from Sharif University of Technology, where he studied from 2006 to 2011. He furthered his education by obtaining two Master's degrees: one in International Business from Hult International Business School in 2012 and another in Finance from The George Washington University in 2013. His academic background provides a strong foundation for his expertise in quantitative credit risk management and financial modeling.
Background
Before joining City National Bank, Sam Rezaei worked as a Consultant at The World Bank from 2013 to 2015. His experience there contributed to his understanding of global financial systems and risk management practices. This role, combined with his educational background, has equipped him with the skills necessary for his current position in the banking sector.
Achievements in Financial Modeling
Throughout his career, Sam Rezaei has developed several key financial models that enhance risk assessment processes. He created revised Probability of Default (PD) and Loss Given Default (LGD) models using SQL, Excel, and VBA. Additionally, he produced a consumer stress testing model in Excel for the 2016 DFAST consumer stress testing. His work in developing original Loss Emergence Period (LEP) and Exposure at Default (EAD) models for ALLL demonstrates his proficiency in quantitative analysis.