A model risk review evaluates an AI/ML model's risk before and during use, performance, bias, robustness, explainability, and drift, proportionate to its risk tier. This is the Model Risk Review from the Stratenity Library, a governed strategic execution asset that is already built for you. It gives your team the full working method for model risk review, and it walks through everything from data lineage and input quality to explainability and model documentation. Every asset ships with a worked client example, a clear RACI, defined KPIs, and governance you can defend, so your team adapts it to a live client engagement instead of starting from a blank page.
What this asset covers
This is a preview of the full deliverable. Inside the Stratenity Library, this asset walks your team through the complete method, section by section, with a worked client example threaded throughout.
- Review Scope and Risk Tiering
- Model Purpose and Intended Use
- Data Lineage and Input Quality
- Methodology and Design Soundness
- Validation and Back-testing
- Bias, Fairness and Discrimination Testing