May 21, 2020 | 60 Minutes
Hosted by American Banker

​AI is now a key competitive edge for financial firms. Many use AI models to improve underwriting decisions, prevent fraud, optimize liquidity, cut costs, increase profitability, and more. However supervisory guidance such as the Federal Reserve Board’s SR-11-7 requires that AI models be tested and managed for risk.

Unlike rule-based models, AI models are a "black box" because their outcomes are more difficult to explain. In this webinar, executives from RBC, a bank known for its AI initiatives, will discuss the challenges of AI model risk management (MRM). Learn from an AI expert at IBM how model validation can be faster and easier, with customizable validation tests that can:

• Run automatically across the model lifecycle on-prem or any cloud
• Mitigate bias and drift
• Explain AI outcomes
• Compare models side-by-side
• Automatically sync test results with systems of record

Watch this web seminar on-demand to explore AI model risk management and learn how to simplify and automate model validation, empowering financial firms to accelerate and scale better outcomes from AI.

​Speakers:

Greg Kirczenow Senior Director, Enterprise Model Risk Management Team RBC (Speaker)	​Zain Nasrullah Data Scientist, Enterprise Model Risk Management Team RBC (Speaker)	Alex Jones Offering Manager IBM Watson OpenScale (Speaker)	​Alan Drummer Content Marketing Manager IBM Data Science and AI (Moderator)

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