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Topic

Model Risk Management Workshop

Abstract

MRM Workshop - 4hrs - Oct 13th 

Abstract
 
In most financial institutions, the data science and model risk/validation functions are under constant pressure to produce and update accurate governance reports. With increasing adoption of machine learning and regulatory scrutiny, the complexity of risk management and governance reporting is rapidly increasing.   The three lines of defense need to satisfy internal and external validation and audit requirements, including those defined in the Federal Reserve’s SR11-7 guidelines.    In response, modern ML Platform teams are developing automated governance workflows integrated with MLOps, which are specifically designed to improve model quality and time-to-market.  This workshop provides valuable updates, discussions and demonstrations of model risk management requirements, challenges and solutions by industry experts.   
 
 
Agenda - 4 hours, virtual 
Machine Learning in Banking: Applications, Model Risk, and Explainability!
Agus Sudjianto, EVP and Head of Corporate Model Risk, Wells Fargo
 
Risk management for pricing, forecasting and time-series models. 
Nikola Gradojevic, Professor of Finance, at the University of Guelph 
 
Governance-Driven Strategies for Responsible AI/ML
David Van Bruwaene, CEO-Fairly.ai
 
Governance Workflow Demonstration
Stuart Maiden, Data Scientist, Fairly.ai  
 
ML Platform and governance reporting advantages of MLOps with Kubeflow
Josh Bottum, Kubeflow Community Product Manager and VP-Developer Relations, Arrikto, Inc.
 
Model Risk Management Round Table
Agus Sudjianto, EVP, Head of Corporate Model Risk at Wells Fargo
Nikola Gradojevic, Professor of Finance, at the University of Guelph
David Van Bruwaene, CEO-Fairly.ai
Josh Bottum, Kubeflow Community Product Manager

Profile

David Van Bruwaene is the CEO and co-founder of Fairly AI Inc.