A Unified Approach to Model Governance Using SAS Model Manager and SAS Model Risk Management

In today’s regulatory and data-driven environment, financial institutions must not only build high-performing models—they must also manage them with transparency, accountability, and control. SAS Model Manager and SAS Model Risk Management (MRM), both built on the SAS Viya platform, offer a powerful integration that bridges the gap between model deployment and enterprise governance.

What Is SAS Model Manager?

SAS Model Manager is a centralized platform for managing the full lifecycle of analytical models. It supports model registration, versioning, validation, deployment, and performance monitoring. Whether models are built in SAS, Python, R, or other frameworks, SAS Model Manager provides a consistent and scalable way to operationalize them across environments.

What Is SAS Model Risk Management?

SAS Model Risk Management is designed to help organizations govern their models. It provides tools for model inventory, validation workflows, risk assessments, documentation, and audit trails. MRM ensures that models meet internal policies and external regulatory requirements, such as SR 11-7, CECL, IFRS 9, and Basel guidelines.

How They Integrate

The integration between SAS Model Manager and SAS MRM creates a seamless bridge between operational model deployment and enterprise-level governance. This is achieved through native platform services and shared metadata, enabling real-time synchronization and oversight.

Key integration features include:

Model Linkage and Metadata Sync: Models registered in SAS Model Manager can be linked directly to SAS MRM. Once linked, metadata such as model type, owner, version, and validation status is synchronized automatically. This linkage enables a “Model Risk Card” to appear in Model Manager, displaying risk assessments, findings, and upstream/downstream relationships.
Unified Inventory and Governance: SAS MRM maintains a centralized inventory enriched by SAS Model Manager’s operational data. This creates a single source of truth for model lineage, ownership, and usage across the enterprise.
Workflow Automation: Updates in SAS Model Manager can trigger governance workflows in MRM, such as materiality assessments, approvals, and documentation updates. These workflows are orchestrated using SAS Workflow Manager and BPMN processes.
Performance Monitoring and Risk Assessment: SAS MRM tracks model drift, KPI thresholds, and validation timelines. These insights are surfaced in SAS Model Manager dashboards, allowing model owners to take corrective action when needed.
Visual Relationship Mapping: The integration enables visualization of upstream and downstream model dependencies. This helps risk teams understand how changes to one model may affect others in the ecosystem.

Technical Architecture

The integration between SAS Model Manager and SAS Model Risk Management is powered by the SAS Viya platform and its underlying Risk Cirrus architecture. This foundation enables seamless communication, governance, and scalability across both systems, ensuring that models are not only operationally effective but also governed with rigor and transparency.

At the core of this integration is the Risk Cirrus Core and Object Framework, which manages the persistence of model metadata, the mapping of relationships between models, and the orchestration of deployment services. This framework ensures that every model—whether statistical, machine learning, or AI—is tracked with full lineage and context, allowing teams to understand how models interact and evolve over time.

Customization is handled through Cirrus Builder, a no-code interface that allows users to tailor workflows, dashboards, and user interfaces to their organization’s governance policies. Whether it's configuring validation steps, approval chains, or attestation forms, Cirrus Builder empowers risk and compliance teams to adapt the platform to their unique needs without relying on developers.

Communication between SAS Model Manager and SAS Model Risk Management is facilitated by RESTful APIs, which enable real-time data exchange. These APIs allow metadata, performance metrics, and governance triggers to flow between systems automatically. For example, when a model is updated or redeployed in Model Manager, the change can instantly initiate a validation workflow in MRM, ensuring that governance remains tightly coupled with operational activity.

Security and access control are enforced through protocols and role-based access management. This ensures that only authorized users can view, edit, or approve models, and that sensitive data is protected across both platforms. Each user’s permissions are aligned with their role—whether they’re a model developer, validator, reviewer, or risk officer—ensuring accountability and compliance at every stage.

Together, these architectural components create a robust and flexible environment where model lifecycle management and risk governance are fully integrated. This not only streamlines operations but also strengthens regulatory posture and organizational confidence in model-driven decision-making.

Real-World Usage

In practice, organizations are using this integration to:

Automate model validation workflows triggered by changes in Model Manager.
Centralize model inventory with synchronized metadata across both platforms.
Visualize model lineage and dependencies to support impact analysis and change management.
Monitor performance and compliance using integrated dashboards and scheduled jobs.
Improve user engagement through notifications, role-based access, and attestation reminders.

These capabilities are helping institutions move from fragmented, manual governance to a unified, automated model risk management framework. The result is greater confidence in model decisions, faster response to regulatory changes, and improved collaboration between data science, risk, and compliance teams.

Summary

The integration of SAS Model Manager and SAS Model Risk Management offers financial institutions a unified and robust framework for managing the entire model lifecycle—from development and deployment to governance and oversight. By synchronizing metadata, automating validation workflows, and enabling real-time performance monitoring, this integration ensures that models are not only operationally effective but also compliant with internal policies and external regulations. It empowers teams to collaborate across data science, risk, and compliance functions, streamlining processes and reducing manual overhead. With centralized visibility into model lineage, usage, and risk posture, organizations can make faster, more informed decisions while maintaining transparency and accountability. Ultimately, this integration transforms models from isolated technical assets into governed, strategic tools that drive business value and regulatory confidence.

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