Applying existing model risk management (MRM) guidance to AI/ML-based risk models ensures financial institutions can leverage innovation without undermining safety or compliance. The paper emphasizes that AI/ML models are not inherently riskier than conventional models; their risk depends on application, complexity, and materiality. Key recommendations include prioritizing robust, ongoing testing over explainability, tailoring documentation standards (especially for third-party models), and promoting global standards such as NIST and ISO. Governance tools like incremental rollouts and circuit breakers, coupled with regulator–industry collaboration and training, are critical to strengthening oversight and mitigating emerging risks.

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