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Overview
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.
Learn more
Five ways to simplify model risk management
Enhance model compliance with custom tests and thresholds.
Podcast: KPMG-IBM on AI
Listen to AI experts discuss digitization of governance in the age of AI.
Product images
Risk model evaluations
Risk model evaluations
Show fairness, quality and drift metrics. Flag models below custom thresholds. Drill down for details.
Fairness
Fairness
Configure and perform model validation. Test model metrics including model fairness.
Model comparison
Model comparison
Compare model test results. Select and speed the development of more effective models.
Metrics summary
Metrics summary
Generate a fact sheet in PDF automatically. Summarize model details, relevant data and test results.
Resources
Get started with explainable AI
Explore model monitoring and model management in IBM Watson Studio.