AI/ML in Banking - The Rise of the Machines

As a Quantitative Risk Consultant at Darling Consulting Group, Sam has validated a variety of risk models for large financial institutions—including risk rating, stress testing, allowance and deposit models—from both a statistical and business perspective. Sam has also combined his background in econometrics with his experience in credit risk to help DCG enhance its community bank credit stress testing methodology.

Before arriving at DCG, Sam served as a Senior Consultant in SunGard’s Risk & Performance Group, where he developed models in multiple areas of financial risk, with a focus on credit and interest rate risk. Sam designed SunGard’s Dodd-Frank Act stress testing model selection algorithm and has also created custom PD and LGD models, including a suite of models currently implemented at a top 15 U.S. bank (by asset size).

Sam graduated cum laude with a bachelor’s degree in economics with mathematical applications from Princeton University. While at Princeton, he was the recipient of the John Glover Wilson Memorial Award for his thesis studying the economics of bargaining.