The financial services industry is undergoing a considerable transformation, driven by rapid technological advancements, shifting regulatory expectations, and evolving consumer behaviors. As a result, Model Risk Management (MRM) needs are changing, and MRM programs need to adapt. Most recently, change has been driven by the increasing use of artificial intelligence (AI), the need for more responsive models, and growing model inventories with inherent complexities, all of which are constantly reshaping the model risk landscape. AI-powered models promise efficiency, increased accuracy, and innovation, but they also introduce new dimensions of risk, including bias, interpretability challenges, and unforeseen vulnerabilities. Less dynamic and/or poorly developed models cannot support strategic and effective decision making long term. Large and unwieldy model inventories can increase risk when less effective MRM programs struggle to maintain adequate oversight. Competitive maintenance is required in today’s fast-paced environment, but the goal is competitive advantage. Neither strategy will work without effective risk management.
As financial institutions embrace advancements, fundamental MRM activities remain critical. In an era where decisions are increasingly data driven, organizations cannot afford to overlook rigorous validation, strong governance, and monitoring of their models. Failing to maintain strong risk practices could lead to financial instability, compliance breaches, and reputational damage, at a minimum. Balancing innovation with accountability is no longer optional. Sustained success in an increasingly complicated financial environment is now undeniably predicated on responsibility and accountability.
Foundational MRM Pillars
Governance & Oversight
Strong model governance and oversight drive accurate, fair, and responsible decision making. Without rigorous oversight, models can introduce bias, rely on flawed assumptions, or become outdated. Organizations should regularly assess their governance frameworks by reviewing accountability structures, validation protocols, and monitoring mechanisms to help ensure compliance and effectiveness, for both internally and externally developed models.
To enhance model oversight and enable innovation, organizations should:
- Empower governance committees to make informed, risk-aware decisions
- Integrate model risk frameworks with broader enterprise risk strategies
- Establish clear, adaptable policies that foster innovation while maintaining compliance
- Implement training programs to promote risk awareness across teams
- Conduct routine audits to help validate operational alignment with regulatory requirements
- Deploy a structure that supports accountability and follow-through
Model Identification, Determination, Classification, & Inventory
Accurate model identification and determination are critical to managing risk, ensuring compliance, and optimizing decision making. Organizations must distinguish between models that apply statistical, machine learning, or predictive techniques and simpler tools that assist with calculations or reporting. Without clear classification protocols, companies may overlook critical validation and governance requirements, elevating model risk and increasing exposure to flawed or biased outputs.
To strategically align time, talent, and technology to detect, measure, and mitigate risk, organizations should:
- Define standardized classification criteria to streamline model risk assessments
- Require completed model determination templates for consistency across business units
- Implement automated tracking systems that enhance transparency and control
- Conduct continuous audits to detect overlooked models and maintain regulatory compliance
Development & Documentation
As models increase in complexity, which is the case for AI and machine learning (ML) methodologies, and model inventories continue to expand, clear, detailed, and effective model documentation becomes an asset for developers, users, validators, and auditors. Transparency in model development fosters trust, allowing organizations to identify potential biases, enhance collaboration, and align with ethical and regulatory standards. Beyond simply detailing how the model works, documentation promotes accountability, accuracy, and an overall understanding through clearly documented assumptions, limitations, weaknesses, model use, and data-sourcing information. Without clear documentation standards required by MRM, models may be poorly designed, misinterpreted, or difficult to validate, increasing operational and regulatory risk. Detailed documentation helps provide a clear audit trail, ensures reproducibility, and enables stakeholders to “allow parties unfamiliar with a model to understand how the model operates, as well as its limitations and key assumptions,” as guided by SR 11-7.
In order to align innovation with audit readiness and facilitate a culture of accountability across each of the three lines of defense, organizations should:
- Establish clear development protocols that encourage experimentation within risk parameters, and set clear and consistent expectations for model developers
- Maintain detailed documentation that follows a prescribed outline to help with transparency and reproducibility, and manage regulatory scrutiny
- Cultivate cross-functional collaboration to integrate diverse perspectives into model design
Validation & Effective Challenge
Model validation can help organizations identify weaknesses, mitigate risks, and enhance decision making by rigorously testing model assumptions, data integrity, calculations, and performance. Validation procedures should include a robust evaluation of the models’ inputs, processing components, and outputs, as well as ensure model developers and users are compliant with internal procedures, such as, but not limited to, documentation requirements, access controls, and change management protocols. Model validation and effective review and challenge help ensure model accuracy, reliability, and alignment with regulatory expectations, and model validation procedures should be adjusted to account for new or emerging risks as organizations face change.
For models to not only be designed and deployed appropriately but also able to adapt to change, organizations should:
- Develop a validation approach and structure that prioritizes high-risk models, based on a defined risk tiering process, and confirm that models are subject to review in off-cycle validation years
- Incorporate an assessment not only of the model’s inputs, processing components, and outputs, but also compliance with internal policies and procedures that govern development, deployment, and use
- Make sure models have sufficient transparency and repeatability to detect potential pitfalls and that proper mitigation strategies are in place
- Conduct rigorous model challenge exercises to reinforce governance effectiveness
- Confirm adequate model validation staff possess the necessary knowledge, skills, and experience and have authority and stature to provide effective challenge
- Perform stress testing, back testing, and sensitivity analysis to help ensure ongoing model performance
Performance Monitoring & Change Management
Ongoing model monitoring is essential for models to remain accurate, reliable, and aligned with business objectives and regulatory requirements. Without continuous oversight, models can degrade due to shifting data patterns, market conditions, or operational changes, leading to poor decision making and increased risk exposure. As markets continue to rapidly change and consumer behavior shifts, key performance indicators (KPIs) such as predictive accuracy, stability metrics, and bias detection can help organizations determine when a model needs recalibration or replacement. Establishing strong expectations for ongoing monitoring helps ensure accountability, encourages proactive risk mitigation, and enhances model performance over time.
Ongoing monitoring helps ensure models remain aligned with business objectives and evolving market conditions. To enhance adaptability and accountability, organizations should:
- Use predictive accuracy and stability metrics to assess model effectiveness
- Establish thresholds for model performance to measure potential need for model change
- Implement structured change management protocols to facilitate seamless model recalibration
- Enable audit readiness by maintaining historical performance records and validation reports
- Inform model risk oversight committees to support informed decision making while also increasing transparency and accountability
Third-Party & Vendor Model Oversight
Relying on third-party vendor models introduces significant risk, including lack of transparency, data security concerns, and potential misalignment with an organization’s risk tolerance and regulatory requirements. While vendor models can bring efficiency, without proper oversight, companies may unknowingly deploy models with biased assumptions, poor governance practices, or hidden vulnerabilities, leading to financial and reputational damage. Strong third-party risk management helps ensure external models undergo rigorous vetting, including thorough due diligence, contractual safeguards, and ongoing performance monitoring. Organizations must establish clear expectations for vendor accountability, requiring regular audits, sufficient documentation, and compliance reviews to mitigate risk and maintain operational integrity. Third-party vendors cannot adequately evaluate each organization’s specific model use, which furthers the need for independent validation of vendor models to reduce dependency on company-agnostic vendor-driven validation.
To help mitigate operational and security risks, financial institutions should:
- Enforce vendor accountability and transparency that includes service-level agreements and expectations for internal review and challenge
- Develop business continuity plans in the event of an outage or vendor failure
- Verify data sourcing, implementation specifications, access controls, and change management protocols, at a minimum
- Conduct regular audits and performance reviews to validate external model reliability
- Implement due diligence frameworks that align vendor practices with enterprise risk strategies
AI/ML Risk Management
AI and ML techniques introduce unique risks, including opacity, bias, and lack of interpretability. Unlike traditional models, AI/ML models often rely on complicated algorithms that may lack transparency, making it difficult to identify errors or unintended biases. Without governance, AI-driven models can evolve unpredictably, potentially producing unreliable or noncompliant outcomes.
AI-driven models must balance innovation with compliance to maintain ethical and strategic alignment and organizations should:
- Confirm AI models are interpretable and explainable
- Develop ethical AI guidelines that align with regulatory and business expectations
- Deploy robust validation and monitoring mechanisms for real-time risk mitigation
- Safeguard strategic alignment between the AI program, MRM, Enterprise Risk Management, and other key businesses, e.g., IT, security, etc.
- Empower personnel with training to support an understanding of AI, the organization’s acceptable use policy, and overarching governance expectations
Conclusion
As financial institutions embrace evolving technologies, shifting regulatory focus, and the need to react to an ever-changing market, robust MRM remains essential to balancing innovation with accountability. Strategic governance, continuous validation, and effective oversight help models remain transparent, reliable, and compliant amid rapid change. Organizations that prioritize collaboration and accountability, audit readiness, proactive monitoring, and ethical model practices can fortify financial stability while supporting innovation. In an era of rapid transformation, responsible MRM is not just a best practice, nor is it a simple check-the-box compliance exercise. Strong model risk management is foundational for sustained success. For more information and in-depth insights, watch our webinar archive, Navigating AI Risk & Governance in Your Institution. If you have questions or want to learn more about our services, please reach out to a professional at Forvis Mazars.