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 Central Bank of the UAE (CBUAE), through its Model Management Standards and Guidance, emphasises the importance of MRM for financial institutions. This emphasis aligns with global regulatory authorities, such as the European Central Bank (ECB) alongside the CBUAE, and the existing regulatory framework, which outlines expectations for institutions to demonstrate enterprise-wide model risk control.
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.
Supervisory guidance, such as the MMSG, stresses the need for multiple layers of defence. This is highly elaborated within section 10 of the standard and highlights the importance of independent validation by either the LFIs’ internal audit function, or through an external party.
For instance, if the Credit Risk Control Unit (CRCU) provides the first view of model performance, the validation team within the MRM should then offer a second objective assessment. Additionally, the internal audit function must validate independently and report any findings to the Board Audit Committee. This separation strengthens objectivity, limits incentives to obscure weaknesses, and ensures models are reviewed by teams not involved in development.
The board, model oversight 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.
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.
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 Al: 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.
Efficiency gains can be achieved through smart tiering and prioritisation mechanisms. Leveraging monitoring results can help skip full validations when performance is stable.
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.
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.
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.
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.
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.
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 Al or trading portfolios.
- Outsourced is a cost-efficient option for standardised or legacy models, provided that 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
MRM has evolved from a compliance framework into a strategic and forward-looking capability. As model portfolios expand and regulatory demands increase, banks need MRM frameworks that are both robust and practical. Clear governance, disciplined validation and integrated monitoring provide the control, transparency and consistency that regulators expect, while supporting effective decision‑making across credit, capital and financial planning.
Well-designed MRM frameworks allow banks to manage model risk with confidence and at scale. By strengthening these capabilities, institutions can improve the quality of their modelling outcomes, allocate capital more effectively and maintain trust with supervisors, boards and stakeholders.
How we can help
For banks seeking to enhance their MRM capability, specialist quantitative risk management support can play a critical role.
Grant Thornton UAE supports financial institutions across the full model lifecycle, combining deep technical expertise with practical regulatory insight. Our teams draw on a network of more than 60 professionals across the US, EU and MENA, offering a broad international perspective informed by local supervisory requirements.
We have experience supporting institutions in their engagement with the CBUAE and the ECB, helping banks design, enhance, and operate MRM frameworks that meet regulatory expectations and support sound, well‑governed decision‑making.
If your priority is to build a robust, scalable MRM framework that meets supervisory expectations and supports confident decision-making, reach out to our experts to discuss how we can help.
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Anand Balasubramanian Anand brings over 25 years of consulting experience across the GCC, APAC, and US, with a strong focus on analytics, risk advisory, and ESG. He has built a strong reputation and network in the region over the last 16 years, particularly within the financial services sector, advising central banks, regulators, DFIs, and commercial banks on risk analytics and strategy, climate stress testing, regulatory inspections, and AI-ML driven digital transformation of risk functions.View Profile -
Ben Marshall Ben has worked in leading financial institutions including Morgan Stanley and Bank of America Merrill Lynch, gaining substantial experience in banking and capital markets. His expertise ranges across regulatory compliance, prudential risk and credit risk advising pillar banks, global asset managers and regulatory authorities. At Grant Thornton Ireland, Ben led advisory engagements with a focus on credit risk, market risk, operational risk, model risk management and quantitative risk across major Irish, British and US financial institutions.View Profile -
Jonathan Fitzpatrick Jonathan is a partner in advisory and head of quantitative risk. Jonathan specialises in delivering risk measurement and quantification services to clients. He has over 16 years’ experience, working in both the UK and Ireland, where he led teams across professional services firms and in multinational banks.View Profile -
Lukas Majer Lukas is a director and head of quantitative risk in Spain. He has extensive experience in both traditional and novel risk modelling, covering credit Risk, interest rate risk, market risk, climate risk, and AML risk. His expertise spans the full risk lifecycle across all three lines of defence. He has worked with both banks and regulators, providing him with a comprehensive perspective on supervisory expectations and industry best practice.View Profile