Model Risk Management
Govern AI Models from Development Through Retirement
AI models that are not properly governed create financial, operational, and reputational risk. GRAVITI implements model risk management frameworks that provide validation, monitoring, and lifecycle governance for every model in your portfolio.
Microsoft Azure
Amazon Web Services
Google Cloud
IBM Cloud
Oracle Cloud- Full flexibility in deployment options. We are not commercial partners of software vendors
Who Is It For
Model risk management is essential for organizations deploying AI models that influence financial decisions, customer outcomes, or operational processes.
- Model risk officers and validation teams responsible for AI model governance
- Data science leaders managing growing portfolios of production models
- Chief Risk Officers seeking enterprise-wide visibility into AI model risk exposure
- Regulated industries (financial services, healthcare, insurance) with supervisory model governance requirements
Our Approach to Model Risk Management
GRAVITI builds model risk management infrastructure aligned with SR 11-7 principles and adapted for modern AI and machine learning models. We implement the three lines of defense—model development standards, independent validation, and audit oversight—with technical tooling that scales across your model portfolio.
Our engineers deploy model inventory systems, automated validation pipelines, performance monitoring dashboards, and documentation generators that give your risk team complete visibility into every model's development, testing, deployment, and ongoing performance. Each model receives a risk tier classification that determines the rigor of validation and monitoring requirements.
We also build model lifecycle management workflows that govern approvals for production deployment, trigger re-validation when performance degrades, and manage model retirement when systems are decommissioned or replaced.
Connecting to systems already in your organization
Our solutions include integration with popular market systems, as well as any additional system as needed
How We Deliver
- Model Inventory: Catalog all AI/ML models with risk classification, ownership, and dependency mapping
- Validation Framework: Implement automated model validation pipelines with challenger model benchmarking
- Performance Monitoring: Deploy real-time model performance dashboards with drift detection and degradation alerts
- Lifecycle Governance: Build approval workflows for deployment, change management, re-validation, and retirement
- Documentation & Reporting: Automate model documentation generation and risk reporting for governance committees
Expected Outcomes
- Complete model inventory with risk-tiered governance and validation requirements
- Automated model validation reducing manual review cycles by 50-70%
- Real-time performance monitoring with early detection of model drift and degradation
- Regulatory-grade documentation and audit trails for all production models
Service Model
- Assessment: 3-week model inventory and risk management maturity assessment
- Build: 10-16 week MRM framework implementation, tooling deployment, and workflow configuration
- Managed: Ongoing validation support, monitoring operations, and regulatory change management
Frequently Asked Questions
How do you classify model risk tiers?
We use a multi-factor classification framework considering model materiality (financial impact), complexity (algorithm type, data sensitivity), usage (decision automation level), and regulatory exposure. Each tier maps to specific validation rigor, monitoring frequency, and documentation requirements.
Does this apply to third-party vendor models?
Yes. Vendor and third-party models require risk assessment and ongoing monitoring under most regulatory frameworks. We implement vendor model governance including initial due diligence, performance benchmarking, and ongoing output monitoring even when model internals are not accessible.
How do you detect model drift?
We monitor multiple drift indicators: input data distribution shifts (data drift), prediction distribution changes (concept drift), and performance metric degradation against validation benchmarks. Alerts are triggered when drift exceeds configurable thresholds, prompting re-validation or retraining.
Is this aligned with regulatory requirements?
Our framework aligns with SR 11-7, EBA guidelines on internal governance, the EU AI Act's risk management requirements, and NIST AI RMF. We customize implementation to your specific regulatory obligations and supervisory expectations.
Govern Your AI Model Portfolio
Unmanaged AI models are a growing risk. Let GRAVITI implement the model risk management framework that gives your organization control, visibility, and regulatory confidence.
More in AI Governance Framework
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