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. In this talk Agus will share how to bring explainability and robustness to machine learning models in production in the finance industry.

Explainability is critical to evaluate conceptual soundness of models particularly for mission critical applications such as in highly regulated financial institutions.

There are many explainability tools available and the speaker's focus in this talk is how to develop fundamentally interpretable models without sacrificing model performance.

Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models.

Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against environment changes are critical, particularly when models are deployed in adversarial environment.

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