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:
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Five ways to simplify model risk management
Enhance model compliance with custom tests and thresholds.
Podcast: KPMG-IBM on AI
Listen to AI experts on digitization of governance in the age of AI.
Blog: Learn about model risk management
Validate AI models and compare models side by side.
Product images: Model risk management
Generate a fact sheet in PDF automatically. Summarize model details, relevant data and test results.
Show fairness, quality and drift metrics. Flag models below custom thresholds. Drill down for details.
Configure and perform model validation. Test model metrics including model fairness.
Compare model test results. Select and speed the development of more effective models.
Generate a fact sheet in PDF automatically. Summarize model details, relevant data and test results.
Inside model risk management
Explore model risk governance
Get a quick guided tour of how to automate tests and automatically document results.
Client webinar: RBC on model risk management
Explore how RBC tackled model validation challenges with automated model validation.
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.