Explore the analyst report

IBM is a Leader in The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning, Q3 2020

What is AI model risk management?

The Federal Reserve and Office of the Comptroller of the Currency guidance SR Letter 11-7 (link resides outside IBM) defines a model as "…a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates."

Model risk can occur when a model is used to predict and measure quantitative information but the model performs inadequately. Poor model performance can lead to adverse outcomes and result in substantial operational losses. Implementing model risk management in a modern information architecture helps you:

  • Speed time to help meet regulatory compliance and other risk objectives.
  • Simplify model validation across multiple clouds.
  • Take advantage of models and data running virtually anywhere.

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Five ways to simplify model risk management

Enhance model compliance with custom tests and thresholds.

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Podcast: KPMG-IBM on AI

Listen to AI experts on digitization of governance in the age of AI.

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Blog: Learn about model risk management

Validate AI models and compare models side by side.

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Inside model risk management

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Explore model risk governance

Get a quick guided tour of how to automate tests and automatically document results.

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Client webinar: RBC on model risk management

Explore how RBC tackled model validation challenges with automated model validation.

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Documentation: Model risk management

Get a step-by-step overview of model risk management.

Get started with explainable AI

Explore model monitoring and management for insights that improve AI outcome confidence.