Model risk: Turning compliance into strategic capability for banks

Models now influence nearly every critical decision in a bank. They help shape credit approvals, stress testing, and pricing. As the volume and complexity of these models grow, banks are rethinking how to balance risk and reward.

If models are inconsistent or poorly governed, they could lead to increased provisions, unnecessary capital consumption, and eroded trust. A strong Model Risk Management (MRM) function mitigates this by balancing control and agility. It brings governance, automation, and data quality into a single, transparent framework.

Supervisors are raising the bar. The ECB, PRA, and Federal Reserve now expect institutions to demonstrate enterprise-wide model risk control. Meanwhile, the EU AI Act extends governance expectations to algorithmic models, including demands for transparency, explainability, and ongoing monitoring.

An efficient MRM framework keeps models accurate, compliant, and useful for decision-making. It gives management and regulators confidence that capital allocation and financial forecasts rest on reliable foundations.

Banks that modernise their MRM frameworks will not only meet supervisory expectations but also turn model risk into a source of competitive strength, driving more accurate provisioning, smarter capital allocation, and faster, well-governed decision-making.

Core pillars of modern MRM

A modern MRM framework needs more than policy statements. It depends on clear roles, a complete view of the model landscape and a validation process that operates across the entire lifecycle. These pillars create the structure that enables banks to demonstrate control, respond quickly to issues, and meet rising supervisory expectations.

1. Independent oversight: supervisory guidance, such as the EBA Supervisory Handbook, stresses the need for multiple layers of defence. For instance, if the Credit Risk Control Unit (CRCU) provides the first view of model performance, the independent validation team within the MRM should then offer a second objective assessment. This separation strengthens objectivity, limits incentives to obscure weaknesses and ensures models are reviewed by teams not involved in development. 
2. Robust governance: the board, model risk committee, and model owners must be connected through a formal policy structure. This includes defined escalation paths, regular validation reports, and a clear risk appetite statement. The result is consistent decision-making and faster issue remediation.
3. Transparent model inventory and tiering: efficiency starts with visibility. Banks should classify all quantitative tools (models, near-models, and non-models) and tier them by materiality, complexity, and qualitative impact. High-tier models receive more thorough validation and monitoring; lower tiers receive proportionate oversight. Regulators expect tiering to be periodically reviewed, validated, and recorded accurately within the inventory.
4. Integrated validation and monitoring: validation is a continuous discipline. It tests conceptual soundness, performance, data representativeness, and governance adherence. Modern frameworks embed validation into the model lifecycle, using dashboards to track validation dates, findings, and remediation actions. Regular back-testing, sensitivity analysis, and benchmarking keep models aligned with market and portfolio realities.

Together, these pillars create a structure that supports consistent decisions, strengthens supervisory trust and makes model performance easier to manage across the lifecycle.

From compliance to capability

Supervisory reviews show that governance alone does not deliver an efficient MRM framework. Banks need data discipline, clear processes and technology that supports the full model lifecycle.

The most advanced institutions centralise oversight, automate routine validation steps and tie findings directly to business accountability. This delivers faster closure of regulatory issues and clearer ownership of model quality.

Across the industry, several enablers stand out:

• Structured data management: mapping critical data, improving lineage and quality, and keeping model inputs traceable.
• Automation: triggering validation reminders, generating reports, and flagging performance drift.
• Use of AI: streamlining or automating certain validation or model risk tasks, making these processes more efficient and robust.
• Standardised dashboards: providing real-time visibility of model status, tiering, and findings.
• Embedded governance: integrating validation, monitoring, and change control into business processes.

Implementing these in practice usually requires a centralised model repository. It holds the full inventory, including risk tiers, owners, and validation dates, and serves as the foundation for governance, workflow automation, and consistent documentation.

From there, automated monitoring runs routine statistical checks (such as PSI, t-tests and HHI) to flag drift early and reduce manual effort. Validation dashboards bring the process together by tracking open findings, severity and due dates, giving risk and model owners a shared view of where attention is needed. Hosting these tools within a bank’s own environment keeps data behind its firewall.

The result is a transparent, auditable system that supports faster decisions and better collaboration across risk, finance and audit.

Practical levers

A few targeted practices can make the operating model more scalable without weakening control.

1. As model volumes and complexity grow, scalability is critical. Efficiency gains can be achieved through smart tiering and prioritisation mechanisms. Leveraging monitoring results can help skip full validations when performance is stable. Bundling related model changes (in line with ECB practices) can reduce excessive validation and governance cycles.
2. Introduce tollgates to cut rework. This iterative approach replaces the rigid waterfall process, promoting efficiency by design and reducing rework. Key modelling choices should also be reviewed in advance to ensure regulatory alignment and prevent the development of non-compliant solutions.
3. Focus effort where it matters. A risk-based approach helps teams separate material findings from those that can be mitigated. Assessing issues against available controls, including Margins of Conservatism (MoC), keeps resources focused on weaknesses that genuinely affect model use.
4. Strengthen reporting for faster decisions. Optimising reporting helps governance bodies decide when models can be approved, limited, or decommissioned. Clear escalation procedures can ensure that only issues of appropriate severity are escalated to senior management.
5. Balance risk mitigation and remediation. Shift focus from developing “perfect” models to fit-for-purpose ones. Weaknesses can be mitigated through MoC, for instance, while maintaining acceptable performance for core use cases.
6. Align the second and third lines of defence. Coordination between MRM and internal audit reduces potential overlap and duplication. Shared testing results and reliance mechanisms can streamline assurance, minimise fatigue, and enhance overall effectiveness. 

AI can automate targeted tasks across the workflow, allowing teams to focus on judgement, but banks should introduce it carefully - a subject covered in detail here [link to part two below].

Designing an efficient operating model

Some models require specialist skills and tools, while others can be assessed using adapted versions of existing techniques. Many banks now blend different approaches for model risk management and validation resourcing:

• In-house is an option for maximum control and institutional knowledge but may involve higher ongoing investment and a greater need for skilled staff.
• Co-sourced, also known as in-sourced, blends internal ownership with specialist support for complex or high-risk models, such as AI or trading portfolios.
• Outsourced: cost-efficient for standardised or legacy models, provided oversight and documentation remain within the bank’s MRM governance.

Many banks now blend these approaches, using structured frameworks and shared repositories to maintain uniform quality and auditability across all validations.

What this means for banks

As model portfolios expand and regulatory demands increase, banks need MRM frameworks that are both robust and practical. Clear governance, structured validation, and integrated monitoring give institutions the control, visibility, and consistency that supervisors expect.

The banks that strengthen these capabilities now will be better placed to manage model risk at scale and to use their models with greater confidence in credit, capital and planning decisions.

MRM has evolved from a compliance framework into a strategic and forward-looking capability. Banks that modernise their MRM frameworks will not only meet supervisory expectations but also turn model risk into a source of competitive strength, driving more accurate provisioning, smarter capital allocation, and faster, well-governed decision-making.