Navigating an abudance of AI regulatory initiatives

Navigating a rapidly evolving and complex regulatory landscape can be a challenge. To stay compliant, firms have to investigate and leverage technologies.
Model Risk Management (MRM) tools, such as a model inventory, are widely used technologies in financial services companies. Proven to mitigate risks associated with model use, these platforms accelerate the pace at which AI algorithms can be deployed. To illustrate, when companies adhere to an MRM framework, all models have to pass a certain number of checks before they can be deployed into production. For example, to prove that a model has been properly tested, independent model validators must review whether the model is fit for purpose. These procedures can be modeled in a workflow, and users are then guided through the various steps.

Applying this to AI use cases, this means that before an AI application is put into production, several workflows must be followed and completed. While executing these workflows, MRM tools such as the Yields MRM suite can guide the developer and validator to select the appropriate use case, geography, and governance framework that will have to be applied to a model. The suite will then compute and identify which rules apply and request the necessary documentation and test results to satisfy these rules. This process ensures that all steps are accomplished and in place before deployment.
This approach also allows for the implementation of what are termed preventive controls. Only after all requirements are met and approvals are obtained does an AI model achieve the status necessary for production deployment. This approach guarantees that CI/CD pipelines only deploy models that comply with the established regulations.