Overview
A leading financial institution used SAS Model Risk Management to centralize model inventory, monitor model performance, and establish end-to-end governance processes to reduce model risk and improve decision-making accuracy.
Challenge
The institution faced challenges in tracking, validating, and continuously monitoring many models across business lines. Models varied in complexity, underlying technologies, and regulatory implications. There was no standardized performance tracking, and errors or misuse of models posed high operational, compliance, and strategic risks. Without a centralized system, model lineage, documentation, and risk assessments were fragmented—leading to inefficiencies and increased risk of poor business decisions.
How they solved it
- Established a centralized model inventory using SAS Model Risk Management to document all models and their metadata including ownership, purpose, last review date, compliance requirements (e.g., CCAR, SOX), and model status (active/inactive).
- Assigned model risk ratings based on complexity, use case criticality, assumptions, and regulatory impact to quantify aggregate model risk across the institution.
- Defined performance benchmarks per model using statistical measures such as Gini Coefficient, MSE, and KS Statistic to track model accuracy and health over time.
- Configured thresholds and alerts in the SAS system to automatically flag performance degradation or data input drift, allowing teams to take corrective actions swiftly.
- Integrated automated data feeds into SAS MRM from production environments to calculate and update model performance metrics in near real-time.
- Implemented model lifecycle workflows within the platform—Model Creation → Validation → Approval → Production → Monitoring → Retirement—to ensure consistent governance and transparency at every stage.
- Used SAS Add-In for Microsoft Office to create on-demand and scheduled model performance and validation reports for audit and regulatory reporting.
- Aggregated model risk organization-wide, analyzing dependencies among models that share assumptions or data sources. This helped identify concentration risk and informed strategic decisions about model development or decommissioning.
- Maintained an audit trail for all model changes, validations, and approvals, ensuring alignment with regulatory expectations and simplifying internal/external audits.
- Adapted to regulatory changes by configuring the platform to reflect evolving risk policies and compliance standards, ensuring continuous alignment with best practices.
Result
The institution now has a fully governed model risk environment with centralized visibility and control. Automated performance monitoring has significantly reduced operational risk, while standardized workflows and validation processes have improved compliance and audit readiness. The result is more informed business decisions based on validated and accurately monitored model outputs.
How is SAS a differentiator in this use case?
SAS Model Risk Management provides a comprehensive and centralized solution that integrates model documentation, validation, performance monitoring, and regulatory compliance into a single platform. Its ability to ingest data from various technologies, trigger automated alerts, and support audit-ready reporting makes it a unique, enterprise-grade solution for managing model risk at scale.
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