Failure to adequately explain model development, working and outcome inherently invites both regulatory and customer scrutiny, especially when things go wrong.
- The extent to which customers need to know how and why a particular outcome has been reached
- Do you need to understand black box models and if so, why?
- Where is explainability a luxury and where is it absolute necessity
- Lessons learned from failures and how explainability could have helped
Agus Sudjianto
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. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.
