No matter what modeling tools financial organizations use, they must be monitored, audited, and adjusted over time. A production model that’s not properly monitored and governed presents too many risks for both financial institutions and their customers.
To minimize risk and follow regulatory standards, organizations must develop and implement a model risk management (MRM) framework that incorporates model development, validation, and governance processes. But with the ever-increasing use of machine learning in modeling, financial organizations need to be able to maintain their regulatory compliance, while deploying these increasingly sophisticated models.
That’s why, over the years of working with some of the biggest financial organizations in the world, the DataRobot AI Platform has incorporated sophisticated model risk compliance practices throughout the modeling lifecycle, ensuring best practices and guardrails are put in place to minimize model risk.
Download Automating Model Risk Compliance to find out more about:
- Model risk management best practices
- Capabilities and tools that should be available to financial organizations looking to build transparent model risk practices
- DataRobot AI Platform and its features that directly facilitate adherence to MRM regulations, like SR 11-7
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