All models are wrong and when they are wrong they create financial or nonfinancial harm. Understanding, testing and managing potential model failures and their unintended consequences is the key focus of model risk management. This is a challenging task for complex machine learning models. Key critical enablers to anticipate and manage model failures include model interpretability and robustness. Despite the progress that has been made in explainable machine learning, post-hoc explainers are still fraught with weakness and complexity. In this talk, I will argue that what we need is a self-explanatory — inherently interpretable — machine learning model. I will discuss how to make sophisticated machine learning models. This self-explanatory model construct also facilitates model robustness test and design, a critical aspect when models operate in dynamically changing or adversarial environments.
Presenter: Agus Sudjianto
Dr. Agus Sudjianto is an executive vice president and head of corporate model risk for Wells Fargo, where he is responsible for enterprise model risk management. Prior to his current position, he was the modeling and analytics director and chief model risk officer at Lloyds Banking Group, in the United Kingdom, and senior credit risk executive and head of quantitative risk at Bank of America. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Co. He holds several U.S. patents in both finance and engineering. He holds masters and doctorate degrees in engineering and management from Wayne State University and Massachusetts Institute of Technology.
About this series
The Financial Engineering Seminar Series is a recurring event featuring thought leaders from industry and academia, who bring their experiences to a variety of important topics in this discipline.