Model governance has become crucial for institutions aiming to mitigate risks and ensure regulatory compliance in today’s rapidly evolving financial landscape. It encompasses the framework, processes and practices that financial institutions employ to manage the lifecycle of models, ensuring they align with organizational objectives and regulatory requirements.
Understanding model governance
Model governance is the structured approach to managing multiple compliance models. These models typically consist of three key components: information inputs (data and assumptions), processing (calculations and theories) and reporting (outputs). An effective modeling system can support better decision-making within your organization with reliable model outputs. Leveraging these accurate and reliable models will allow you to allocate resources efficiently, adapt to changes and gain a competitive advantage.
Effective model governance involves multiple stakeholders across the institution:
- Model owners: Typically, in the first line of business, they are responsible for owning the risk and documenting models.
- Risk department: Provides oversight and effective challenge to model owners.
- Information technology (IT) department: Ensures data completeness and accuracy, often integrating models with core systems.
- Audit (Third line): Provides oversight and audits the model risk management program.
Financial institutions must adhere to guidance from regulatory bodies such as the Office of the Comptroller of the Currency (OCC) and the Federal Deposit Insurance Corporation (FDIC). These regulators provide frameworks and guidelines to ensure institutions manage model risks effectively and maintain compliance. Regularly reviewing and comparing policies and procedures against regulatory guidance is essential for maintaining compliance.
Risk considerations
Models can present various risks, including credit, interest rate, price, liquidity, operational, strategic and compliance concerns. Understanding and documenting these risks will help mitigate non-compliance, issues in model validation or monitoring, process inefficiencies and much more.
- Operational risks include data input controls, change management processes and IT interfaces.
- Compliance risks involve fair lending, capital stress testing and anti-money laundering (AML) transaction monitoring.
A common mistake is not ensuring models are free from bias and discrimination, especially for fintech algorithms used in underwriting.
The integration of artificial intelligence (AI) into models introduces new risks and considerations. Institutions must document AI model soundness, decision-making processes and ensure policy procedures incorporate AI risk aspects.
Implementation of a model governance framework
Implementing a model governance framework is important in allowing financial institutions to manage their models throughout their lifecycle effectively. Institutions should have workflows for identifying models, robust documentation, thorough testing and change management processes.
- Governance and oversight: Proper board oversight and approval of policies.
- Model owners' role: Active involvement in validating and tracking model changes.
- Policies and procedures: Clear standards for consistency across the organization.
- Risk assessment process: Effective challenge and risk assessment by model owners.
- Model inventory: Keeping an up-to-date inventory of all models.
- Documentation standards: Detailed documentation of model use, assumptions and limitations.
- Data management: Documenting changes and ensuring data integrity.
- Third-party risks: Proper oversight of third-party models and validations
Common challenges include organizational silos, inconsistent documentation standards, resource constraints and technology integration. Establishing a governance committee and centralizing data assets can help overcome these challenges.
- Establish a governance committee: Include model risk management expertise.
- Develop key performance indicators (KPI) and key risk indicators (KRI): Track model inventory, validation schedules, and performance.
- Comprehensive documentation: Use templates for consistency.
- Training programs: Ensure model owners and risk management teams are well-trained.
Model validation assesses the effectiveness of models by focusing on conceptual soundness, ongoing monitoring and outcome analysis. Regular validation helps identify errors, evaluate performance and build stakeholder confidence.
Mastering model governance requires a comprehensive approach involving multiple stakeholders, robust documentation, regular validation and adherence to regulatory guidance. Institutions can effectively manage model risks and ensure compliance by implementing best practices and addressing common challenges.
