What is model governance?

Model governance originated in the financial sector to address the risks of complex financial models. As artificial intelligence (AI) and machine learning (ML) technologies gained prominence, the relevance of model governance rapidly expanded. According to McKinsey, 78% of organizations report using AI in at least one business function—highlighting just how embedded AI and ML models have become in operational and strategic decision-making. 

The purpose of model governance is to ensure that models—whether traditional financial models or machine learning models—operate as intended, remain compliant and deliver trustworthy results over time. A strong model governance framework supports transparency, accountability and repeatability across the entire model lifecycle.

In regulated industries like banking and insurance, model governance is a compliance requirement. In the United States, the Office of the Comptroller of the Currency (OCC) outlines specific governance practices for managing model risk in financial institutions. While the OCC’s guidance does not carry the force of law, it is used in regulatory examinations. Failure to comply can result in fines or other penalties.

As real-time decision-making becomes the norm and regulatory requirements evolve, effective model governance is emerging as a critical capability for organizations aiming to leverage AI responsibly.

Organizations are increasingly using complex models to support high-stakes decision-making. Whether it's credit scoring in the banking industry or patient risk assessment in healthcare, these models are only as effective as the frameworks that govern them. 

Model governance provides a structure for overseeing the development, deployment and ongoing performance of models. By establishing clear controls and accountability at every stage of the model lifecycle, organizations can ensure their models remain reliable and aligned with business goals. This makes model governance a foundational component of risk management, regulatory compliance and operational integrity. 

Most models—particularly ML models—have become embedded in core business processes. Without proper governance, these models can drift over time, leading to degraded model performance, biased outcomes or decisions that don't align with current market conditions or demographic trends. In sectors like finance or healthcare, these failures can have significant real-world consequences. 

Model governance provides a mechanism to assess and mitigate these risks before they impact business outcomes. Beyond that, organizations can use model governance to:

• Improve regulatory compliance by aligning with requirements from the OCC and other regulatory bodies
• Increase trust among stakeholders by ensuring explainability, consistency and reliability of model outputs
• Optimize operational efficiency through automation and integration of governance workflows

As AI adoption accelerates, model governance also serves as a foundation for ethical AI. It offers a way to embed fairness, accountability and transparency into the design and deployment of models across various use cases. 

A governance framework for models brings structure to what is often a sprawling ecosystem of algorithms, datasets, stakeholders and workflows. While frameworks vary across industries, they typically include the following core components:

Strong governance starts at the source: model development. This component includes defining objectives, selecting training data, validating data sources and ensuring that model inputs are aligned with the intended use case. Data quality is essential here, as flawed or biased inputs can lead to low-quality model outputs.

Model documentation should capture the rationale behind the chosen methodology, the assumptions made, the dataset used and the expected model outputs. This documentation acts as a blueprint for transparency and helps streamline future updates, audits and model validation.

A centralized model inventory allows organizations to track every model in use—along with its purpose, ownership, methodology and status in the lifecycle. This includes financial models, credit scoring algorithms, ML models used for fraud detection and even models embedded in spreadsheets. 

A well-maintained model inventory also supports better risk assessment and facilitates real-time decision-making around model usage.

Validation is a core aspect of model risk management. Independent validation teams test the model against historical data (back-testing), assess sensitivity to dynamic factors such as interest rates or demographic changes and verify that outputs align with business expectations.

For ML models, validation extends to checking for algorithmic bias, robustness and overfitting, which is when an algorithm fits too closely (or even exactly) to its training data and can’t draw accurate conclusions from any other data. The goal is to ensure that model results remain stable and interpretable—even as inputs shift. 

Governance doesn't stop once a model is deployed. Continuous model monitoring is necessary for detecting performance degradation, drift in model inputs or changes in data quality. Observability tools can help track metrics like accuracy and recall, detecting anomalies that may require retraining or recalibration. 

In modern machine learning operations (MLOps) workflows, organizations can automate parts of the deployment process, incorporating governance checks directly into the continuous integration, continuous delivery (CI/CD) pipeline. This enables faster iteration without compromising oversight.

Model governance is a team sport in which data scientists, risk officers, business leaders, compliance teams and auditors are all key players. Defining clear responsibilities and workflows ensures accountability at every stage of the lifecycle—from development to validation to model retirement. 

Effective governance also involves communication. Whether through internal dashboards, governance reports or even a dedicated podcast for cross-functional teams, information must flow efficiently between stakeholders.

The principles of model governance apply across a range of industries, each with its own risks, regulations and priorities:

Model governance is enforced through regional and global regulations that hold organizations accountable for how they manage models across their lifecycle. Notable regulations include:

SR 11-7 sets the standard for model risk management in banking, requiring institutions to maintain a full inventory of models and implement enterprise-wide governance practices. It also mandates that models serve their intended purpose, remain up-to-date and have documentation that is clear enough for independent understanding.

The National Association of Insurance Commissioners (NAIC) introduced model regulations around AI and algorithmic decision-making, particularly as they relate to credit scoring, pricing and demographic fairness. These factors are becoming increasingly critical for insurance underwriting and claims processing governance.

The Artificial Intelligence Act of the European Union, also known as the EU AI Act or the AI Act, is a law that governs the development and/or use of AI in the EU. The act takes a risk-based approach to regulation, applying different rules to AI according to the risk they pose.

Under the General Data Protection Regulation (GDPR), any model that processes the personal data of EU citizens must follow principles like fairness, transparency and accountability. This indirectly impacts ML model governance, especially for explainability and data quality. 

Both the Swiss Financial Market Supervisory Authority (FINMA) and the UK's Prudential Regulation Authority (PRA) have issued guidance on AI and model usage in financial services—FINMA Guidance 08/2024 and PRA Supervisory Statement SS1/23, respectively.

These documents address areas such as model governance, explainability of ML models and comprehensive model documentation. While they share similarities with SR 11-7, each places unique emphasis on aspects like AI-specific risks and operational resilience.​

The Basel Framework outlines principles for effective risk data aggregation and risk reporting (BCB 239), which tie directly into model governance practices like documentation, explainability and model risk oversight. Banks operating internationally often use Basel as a gold standard alongside SR 11-7.

While the value of model governance is clear, implementing it at scale presents several challenges:

• Volume and complexity of models: As organizations adopt more ML and AI tools, maintaining an accurate model inventory becomes difficult—especially when models are embedded in spreadsheets or third-party platforms.
  
  
• Explainability and trust: Many stakeholders, including regulators and business leaders, require explainable models. Balancing performance with explainability remains a persistent tension in AI model governance. 
  
  
• Integration with MLOps: Governance frameworks must adapt to real-time workflows and DevOps-style deployments, without slowing down innovation. 

As AI and ML become more embedded in workflows, new forces are shaping how organizations approach model governance. While foundational practices like validation, model documentation and model monitoring remain essential, several emerging trends are beginning to redefine expectations.

These trends reflect a broader shift: the ongoing evolution of model governance from a defensive approach to a strategic capability. By leveraging structured, cross-functional governance practices, organizations can strengthen trust in their machine learning models while accelerating innovation.