Machine Learning Model Risk Management with Wells Fargo

Ai4
Ai4
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Published 2020-09-15
Agus Sudjianto, EVP, Head of Corporate Model Risk, Wells Fargo

Abstract:
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation. For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness, particularly how to develop fundamentally interpretable models through architecture constraints including deep learning.

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