Mathematical Finance & Financial Data Science Seminar
Machine Learning in Banking
Speaker: Agus Sudjianto, Ph.D, EVP Head of Corporate Model Risk, Wells Fargo & Company
Location: Online Zoom access provided to registrants
Date: Tuesday, December 7, 2021, 5:30 p.m.
The banking industry has rapidly adopted machine learning for various applications. Large banks in the US are typically more cautious in adopting the methodology for high risk and regulated areas such as credit underwriting. The adoption of so called Explainable AI, which is typically ‘black box’ machine learning models accompanied by post-hoc explainability tools, are becoming more common for low risk applications; the concern remains in the high risk area: can we trust post-hoc explainers? Alternatively, there are many recent developments on inherently interpretable, self-explanatory machine learning models without the problem of post-hoc explainers. The latter offers many advantages beyond explainability such as model diagnostics and control to manage model risk. This is the focus of my talk where I will present examples including methods to incorporate model constraints (e.g., monotonicity or other shape constraints) easily and adverse action reason code required by regulation in the US.
Agus Sudjianto is an executive vice president, head of Model Risk and a member of the Management Committee at Wells Fargo, where he is responsible for enterprise model risk management.
Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Before joining Lloyds, he was an 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 Company.
Agus holds several U.S. patents in both finance and engineering. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics.