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Effectively managing model risk is critical as more and more business processes are relying on predictive machine learning models for decisioning. This trend has emphasized the importance of model risk management, which has become a hot topic in the regulatory and compliance-rich industry of financial services.

With Automated Machine Learning, financial institutions now have a tool that will not only automate the building of highly-accurate predictive models, but also automates the documentation required for Model Risk Management. 

This executive briefing covers:

- The challenges - and need - for effective model risk management in today's regulatory environment
- How an automated process delivers efficiency, consistency, and speed to the model risk management workflow
- The importance of challenger model benchmarking to model risk, and how Automated Machine Learning accomplishes it
- An introduction to DataRobot AI Services for Model Risk Management and how they can jumpstart your efforts

Seph Mard - Head of Model Validation, DataRobot

As the head of Model Validation at DataRobot, Seph is responsible for model risk management, model validation, and model governance products, as well as services engagements. Seph is leading the initiative to bring AI-driven solutions into the model risk management industry.

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