Strengthening Model Risk Management: Adapting to New Regulations and Emerging Risk

Financial institutions are under growing pressure from new regulations, stricter oversight, evolving accounting standards, and recent bank failures. These events highlight the urgent need to strengthen Model Risk Management (MRM). It is no longer enough to tick compliance boxes, institutions must embed efficiency, transparency, and resilience into governance by streamlining lifecycles, automating validation, and ensuring continuous monitoring.

Case Study: Lessons from Bank Collapses

The collapse of several U.S. regional banks in 2023 marked a turning point in how both regulators and institutions view and approach risk. These failures exposed weaknesses in liquidity management, interest rate risk oversight, and stress testing. In response, MRM teams at leading banks and financial institutions began rethinking their frameworks.

One large bank, for example, expanded its MRM program by implementing automated validation tools that assess model accuracy within hours rather than weeks. They also introduced real-time monitoring dashboards, powered by machine learning, to flag deviations in credit risk, liquidity, or interest rate models.

This proactive approach enabled the risk committees to address issues promptly, reducing both operational strain and regulatory risk.

This example illustrates how MRM must evolve from a static compliance function into a dynamic, technology-enabled capability center that safeguards resilience and trust.

Why Does It Matter?

#1: Strengthening Core MRM Practices

The foundation of effective MRM lies in efficiency and rigor across the model lifecycle  design, validation, deployment, and monitoring reduce operational overheads as well as reduce human errors and oversight.

• Automated validation tools accelerate testing, allowing teams to focus on higher-value analysis and help make quick decisions without compromising the quality.
• Ongoing monitoring ensures early anomaly detection, leveraging machine learning to flag risks before they cause financial or reputational     damage

#2: Staying Ahead of Regulatory Shifts

Supervisory authorities globally are tightening expectations from their regulated entities.

1. United States: The Federal Reserve continues to build on SR 11-7, with expanded stress-testing scenarios and sharper expectations around model inventory completeness, independent validation, and continuous monitoring.
2. Europe: Regulators are widening their scope to cover climate risk and AI/ML Governance. The proposed EU AI Act sets stringent requirements for high-risk models, adding layers of transparency and accountability.
3. United Arab Emirates: The Central Bank of the UAE has issued Model Management Standards (Notice 5052/2022), requiring firms to establish centralized inventories, governance controls, and lifecycle oversight.
4. Singapore: The Monetary Authority of Singapore (MAS) introduced the FEAT Principles (Fairness, Ethics, Accountability, and Transparency) for AI and data analytics, pushing institutions to operationalize ethical AI governance.
5. Australia: APRA’s CPG 229 on Climate Change Financial Risks and CPG 220 on Risk Management emphasize the integration of emerging risks, including AI/ML, into core governance frameworks.
6. India: The Reserve Bank of India (RBI) has highlighted governance around credit risk models, stress testing, and AI adoption, underscoring the importance of explainability and audit trails.

#3. Addressing Emerging Risks

The regulatory agenda now extends beyond traditional banking risks, bringing new aspects into focus

• AI/ML models must demonstrate transparency, interpretability, and fairness. Techniques such as SHAP and LIME are increasingly used to satisfy interpretability expectations.
• Climate-change risk management requires long-horizon scenario modeling, despite challenges in data limitations. Regulators now expect financial institutions to incorporate climate risk in their stress testing frameworks.
• ESG considerations add yet another layer, requiring institutions to embed sustainability metrics into decision-making processes.

Institutions must align their models with these expanding regulatory expectations while maintaining credibility in their reporting.

#4. Managing Differentiated Standards Across Jurisdictions

For financial institutions operating across borders, one of the biggest challenges in Model Risk Management (MRM) is reconciling diverse regulatory expectations.

• United States: SR 11-7 remains the foundation, reinforced by evolving supervisory expectations around stress testing, independent validation, and continuous monitoring.
• Europe: The EBA guidelines stress proportionality and transparency, while the upcoming AI Act introduces strict accountability requirements for high-risk models.
• United Arab Emirates: The Central Bank of the UAE’s Model Management Standards (Notice 5052/2022) mandate centralized inventories, clear ownership, and lifecycle oversight.
• Singapore: The MAS FEAT Principles (Fairness, Ethics, Accountability, Transparency) and the Veritas initiative set clear expectations for responsible AI and data governance.
• Australia: APRA’s CPG 220 on Risk Management and CPG 229 on Climate Risk highlight the integration of emerging risks, including AI/ML, into MRM frameworks.
• India: The Reserve Bank of India (RBI) continues to strengthen oversight of credit risk, stress testing, and AI-driven models, with emphasis on explainability, transparency, and auditability.

Institutions must align their models with these expanding regulatory expectations while maintaining credibility in their reporting.

#5. Restructuring Resources for Efficiency

Meeting today’s regulatory demands requires rethinking workforce structures. It demands a shift in how institutions organize their people and resources

• Upskilling staff in regulatory compliance, model validation, and AI/ML risks is becoming essential to ensure emerging challenges are covered.
• Hiring specialized talent in areas like climate risk or advanced analytics ensures emerging challenges are covered
• Cross-functional collaboration between MRM teams, compliance, legal, and business units strengthens organizational resilience.

Technology again plays a pivotal role. By deploying advanced validation platforms and regulatory reporting systems, institutions reduce manual workload while ensuring comprehensive oversight.

Enabling Smarter Model Governance with Solytics Partners

Navigating evolving regulatory expectations and managing emerging risks demands more than policies, it requires tools that can keep pace with the scale and complexity of today’s models. This is where Solytics Partners supports institutions through its MRM Vault and AI Vault platforms.

By centralizing all models (Statistical, AI/ML, or Generative AI) into a single inventory, institutions gain clear visibility and ownership, reducing the risk of shadow models or undocumented use. Automated workflows for validation and monitoring ensure governance standards are consistently applied across the model lifecycle, while dashboards provide real-time insights for risk and compliance teams.

For senior management and regulators, the platforms create transparent audit trails and regulator ready reports, demonstrating not only compliance but also a culture of proactive oversight. By aligning with global regulatory frameworks from SR 11-7 in the U.S. to the AI Act in Europe and CBUAE standards in the UAE, MRM Vault helps firms stay ahead of the curve rather than chasing compliance gaps.

Ultimately, Solytics Partners empowers institutions to transform model risk management from a reactive obligation into a forward-looking capability driving efficiency, resilience, and trust in a rapidly shifting regulatory environment.

Vision Ahead

The evolution of Model Risk Management is no longer optional, but an essential paradigm to ensure, financial stability. Institutions that succeed will be those that:

• Build resilient frameworks integrating traditional and emerging risks.
• Leverage technology for transparency and efficiency while aligning with evolving standards.
• Invest in people and cross-functional collaboration to ensure governance keeps pace with regulatory shifts.

The era post the bank collapses serve as a stark reminder that MRM cannot remain static. With regulators pushing forward on AI, climate risk, and stress testing, the vision ahead requires a proactive, technology-enabled, and globally aligned approach to safeguarding financial institutions.

References

‍1. Board of Governors of the Federal Reserve System - SR 11-7: Supervisory Guidance on Model Risk Management
Link: https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm

2. Office of the Comptroller of the Currency (OCC) - Risk Management Guidance
Link: https://www.occ.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdfmodel-risk-management/pub-ch-model-risk.pdf

2. European Banking Authority (EBA) - Guidelines on Institutions’ Stress Testing
Link: https://www.eba.europa.eu/guidelines-stress-testing

3. European Commission - Proposal for the Artificial Intelligence Act
Link: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52021PC0206