2025 Model Risk Management Trends: What Banks Need to Know

2025 Model Risk Management Trends: What Banks Need to Know

As we enter 2025, model risk management (MRM) continues to evolve at an unprecedented pace. While SR 11-7 remains the foundational guidance issued over a decade ago, its application has expanded dramatically to address emerging technologies and increasingly complex modeling environments.

The Enduring Relevance of SR 11-7

SR 11-7, issued jointly by the Federal Reserve and OCC in April 2011 and formally adopted by the FDIC in 2017, established the three-pillar framework for effective model validation:

1. Evaluation of Conceptual Soundness - including developmental evidence
2. Ongoing Monitoring - including process verification and benchmarking
3. Outcomes Analysis - including back-testing and validation

Despite being over 14 years old, this framework remains the cornerstone of model risk management for all federally regulated financial institutions. However, its application in 2025 looks dramatically different than what regulators envisioned in 2011.

Trend #1: AI and Machine Learning Under Heightened Scrutiny

The most significant development in MRM for 2025 is the application of SR 11-7 principles to artificial intelligence and machine learning models. Regulators are now applying SR 11-7's requirements to AI/ML models with heightened expectations around:

Explainability and Transparency

• Models must provide clear explanations of decision-making processes
• "Black box" models face increased scrutiny from examination teams
• Documentation requirements have expanded to include model interpretability analysis

Bias Mitigation and Fair Lending

• Regular bias testing across protected classes is now standard practice
• Institutions must demonstrate ongoing monitoring for disparate impact
• Less discriminatory alternatives (LDAs) must be actively considered and documented

Generative AI Challenges

• Monitoring must capture risks like hallucinations and factual reliability
• Performance under varied or adversarial inputs must be tested
• New validation approaches are needed for gen AI applications

Trend #2: Third-Party and Vendor Model Risk

Regulators are emphasizing third-party and vendor model risk like never before. Financial institutions must now demonstrate comprehensive oversight of external AI services and vendor-provided models.

Key Requirements:

• Independent validation of vendor models (even when vendor provides validation documentation)
• Ongoing monitoring of third-party model performance
• Contractual rights to access model documentation and validation work papers
• Regular reassessment of vendor model risk ratings

Common Pitfalls:

• Accepting vendor validation reports without independent verification
• Inadequate documentation of vendor model governance
• Insufficient monitoring of vendor model updates and changes
• Lack of contingency planning for vendor model failures

Trend #3: Model Inventory Management and Tiering

Model inventory management has become increasingly critical as institutions deploy more models across the organization. Effective model tiering helps allocate validation resources appropriately.

High-Risk Models (Tier 1) require:

• Annual comprehensive validation
• Quarterly monitoring
• Independent validation teams
• Executive-level governance oversight

Moderate-Risk Models (Tier 2) require:

• Biennial validation
• Semi-annual monitoring
• Validation by appropriately qualified staff
• Regular governance review

Low-Risk Models (Tier 3) may have:

• Less frequent validation cycles
• Simplified monitoring protocols
• Streamlined documentation requirements

However, regulators are scrutinizing model tiering methodologies to ensure high-risk models (especially those impacting capital, CECL, or credit decisions) aren't inappropriately classified as lower tier.

Trend #4: Enhanced Documentation Requirements

Documentation standards have evolved significantly. Regulators expect comprehensive documentation packages for each model that include:

• Model Development Documentation: Theoretical foundation, data sources, assumptions, limitations
• Validation Reports: Independent assessment with findings and recommendations
• Ongoing Monitoring Reports: Performance metrics, drift analysis, control testing
• Governance Documentation: Approval records, issue tracking, remediation plans

2025 Best Practice: Maintain a centralized model documentation repository accessible to validators, auditors, and examiners.

Trend #5: Global Regulatory Harmonization

While SR 11-7 remains U.S.-centric, international frameworks are emerging:

• UK SS1/23: The Bank of England's supervisory statement on model risk management
• EU AI Act: Risk-based framework for AI systems in the EU
• Basel Committee Guidelines: Principles for effective model risk management

For multinational institutions, navigating multiple frameworks while maintaining a cohesive MRM program presents both challenges and opportunities for best practice adoption.

Preparing for 2025: Action Items

To stay ahead of MRM trends, institutions should:

1. Conduct an AI/ML Model Inventory

   • Identify all AI/ML models across the organization
   • Assess current validation status
   • Determine appropriate validation frequency

2. Strengthen Third-Party Model Oversight

   • Review vendor contracts for validation rights
   • Implement independent validation procedures
   • Enhance ongoing monitoring protocols

3. Update Model Documentation Standards

   • Implement standardized documentation templates
   • Create centralized documentation repositories
   • Establish clear documentation requirements by model tier

4. Invest in Validator Training

   • Provide AI/ML-specific validation training
   • Develop expertise in bias testing methodologies
   • Build capabilities in explainability assessment

5. Enhance Model Governance

   • Formalize model tiering methodologies
   • Strengthen model risk committees
   • Implement robust issue tracking and remediation processes

The Road Ahead

Model risk management in 2025 is more complex and consequential than ever before. As AI and machine learning continue to transform financial services, institutions must evolve their MRM frameworks to address emerging risks while maintaining compliance with SR 11-7's foundational principles.

The institutions that will thrive are those that view MRM not as a compliance burden but as a strategic capability that enables safe innovation and competitive advantage through better model governance.

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Need help strengthening your model risk management program? RegVizion specializes in SR 11-7 compliant model validation, AI/ML governance, and MRM framework development. Contact us to discuss how we can support your 2025 MRM initiatives.