Model Risk Management in Investment Management

Investment managers have long relied on quantitative models and are increasingly using advanced technologies, AI, and other sophisticated tools to make more efficient, informed decisions on asset allocation, portfolio risk, and market and liquidity risk. Model complexity is growing, and now more than ever investment managers must implement model risk governance frameworks to proactively identify, assess, and mitigate model risk.

Why managing model risk is critical

Model risk refers to the potential for adverse outcomes that result from decisions based on incorrect, poorly designed, or misused models. Importantly, model risk can arise even when a model operates exactly as designed. Models often rely on assumptions, historical data, and simplified representations of markets, and those assumptions might no longer hold as market conditions, asset characteristics, and investor behavior change.

When model risk is not effectively managed, the impact can be significant. Investment strategies might underperform due to flawed signals or optimization constraints. Risk models might underestimate tail risk or fail to capture correlations during stressed markets. Valuation models for illiquid assets might lag market developments and result in pricing inconsistencies and disclosure challenges. Over time, these issues can escalate into regulatory findings, increased client scrutiny, and reputational damage.

Absent a structured model risk management framework, models often evolve informally. Enhancements, overrides, and parameter changes might be implemented without clear approval or documentation. Institutional knowledge can become concentrated among a small number of individuals, which increases key person risk. In many cases, control gaps are identified only after a loss event, a performance anomaly, or a completed regulatory examination.

Applying a risk-based model risk management framework

Although investment managers typically are not subject to the same prescriptive model risk management requirements as banks and certain other regulated financial services organizations, they can adapt established model risk management principles to their processes. A risk-based approach allows organizations to apply discipline and oversight while preserving the flexibility required to support diverse investment strategies and evolving product offerings.

Many model risk management practices are well established in other regulated industries, where independent challenge, clear governance, and disciplined documentation are central to effective risk oversight. Applying these same principles in the investment management context allows organizations to bring greater structure and consistency to model-driven processes without constraining investment decision-making. A focus on appropriate use, governance, and defensibility, rather than technical perfection alone, aligns model risk management with how investment managers operate and how regulators and investors increasingly assess model-driven strategies.

Following are key steps organizations can take to mitigate model risk.

Model inventory: Establishing transparency and ownership
An effective model risk management program begins with a comprehensive model inventory. Investment managers should identify and catalog models used across investment strategy design, portfolio construction, risk management, and valuation. This inventory includes internally developed models, third-party or vendor models, and end-user tools, such as spreadsheets, that materially influence investment decisions.

The practice of inventorying models often provides immediate insight. Organizations frequently discover models that are widely relied upon but poorly documented, inconsistently governed, or informally owned. Establishing a clear inventory improves transparency, clarifies accountability, and creates a foundation for subsequent model risk management activities.

Model risk tiering: Focusing effort on highest risks
Once models are identified, risk tiering helps determine where oversight should be focused. Not all models pose the same level of risk, and a structured assessment based on certain factors, such as impact on investment decisions, complexity, reliance on judgment, and potential client or regulatory exposure, allows organizations to prioritize appropriately.

Models that are central to investment strategies or valuation typically warrant more robust review and monitoring. This approach supports efficient use of resources and reinforces disciplined risk management without introducing unnecessary burden.

Model validation: Performing an independent challenge, not box-checking
Model validation plays a critical role in a risk-based framework, but its purpose is often misunderstood. For investment managers, effective validation is not about achieving theoretical perfection or satisfying a checklist. Instead, it provides an independent challenge of a model’s design, assumptions, data, and intended use.

In practice, validation findings often relate to governance and use rather than flaws in the underlying mathematics or data. Issues such as unclear ownership, undocumented changes, reliance on manual overrides, or misalignment between model design and application are common. Identifying and addressing these gaps helps organizations better understand model limitations and strengthen controls for model development and change management.

Ongoing monitoring: Meeting desired objectives
Even well-designed models can lose effectiveness over time as markets, data sources, and investment strategies evolve. Ongoing monitoring, including outcome analysis, benchmarking, and periodic reassessment of assumptions, helps identify model drift and emerging risks.

Clear escalation and governance processes help organizations identify and address issues in a timely manner, particularly during periods of market volatility, strategy change, or rapid growth, before they translate into material investment or compliance impacts.

Governance and documentation: Supporting defensibility
Strong governance and documentation underpin all aspects of model risk management. Clear ownership, defined approval processes, and disciplined change management promote consistent application of models and support regulatory defensibility.

From a regulatory and investor perspective, the ability to demonstrate how models are governed, reviewed, and updated is often as important as the technical sophistication of the models themselves. Well-documented processes allow organizations to clearly explain their methodologies, assumptions, and controls when questions arise during examinations or due diligence.

Bringing it all together

For investment managers, model risk management provides a practical framework to strengthen governance, improve transparency, and support better-informed investment decision-making.

Key takeaways for investment managers include:

• Growing reliance on models elevates investment and governance risk.
• Model risk most often arises from how models are governed, changed, and used over time rather than from isolated technical flaws.
• A risk-based model risk management framework supports consistency, transparency, and defensibility.
• Established model risk management practices can be adapted without constraining investment flexibility.
• As models become more central to strategy design and execution, questions regarding model governance are becoming more common during regulatory examinations and investor due diligence.

By adapting proven model risk management practices to the investment management context, organizations can apply discipline while preserving flexibility. Those that do not address model risk in a structured and scalable way might find it increasingly difficult to consistently defend their investment processes as models continue to play a central role in the business.