Interpretable Machine Learning Models: Managing data bias, conceptual soundness, implementation and change control

By this time Machine Learning (ML) has been widely adopted in Financial Institutions. The ML model is a non-parametric data driven approach as opposed to its counterpart of the traditional statistical model that has a tendency to be a parametric approach or at least with special care of functional form and input variable selection. To manage the risk of ML models, particularly in a regulated industry such as banks, special attention must be taken on their interpretability to ensure the soundness of the models. As ML models are very flexible to fit the data, unlike its parametric counterpart, models are changing when re-training is applied. Thus, model interpretability is also critical throughout the life-cycle of the models as model re-training is applied due to data shift.

Agus Sudjianto, Ph.D

Executive Vice President, Head of Corporate Model Risk, Wells Fargo & Company

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, 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 a 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 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.


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