Why Strong Model Risk Management Still Matters – Forvis Mazars US

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.
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 Without rigorous oversight, models can introduce bias, rely on flawed assumptions, or become outdated.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:
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:
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:
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:
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:
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:
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:
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.

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