UK's evolving regulatory landscape of Model Risk Management

4 mins read

The complex world of financial regulation is witnessing significant shifts, particularly in the realm of Model Risk Management (MRM). With regulatory bodies intensifying the push for compliance, the imperative for robust MRM frameworks is being increasingly recognized across the UK. This blog delves into the key drivers shaping the MRM regulatory landscape, with a focus on the notable developments in the UK market.

UK market: A paradigm shift in MRM regulation

The UK’s Prudential Regulation Authority (PRA) has set a new benchmark with its recent policy statement (PS6/23), which mandates banks with internal model approval for calculating regulatory capital requirements to implement comprehensive MRM principles by May 17, 2024. This directive covers many risks, including credit, market, and counterparty credit risks. The supervisory statement delineates principles in five crucial areas: Model Identification and Risk Classification, Governance, Model Development, Implementation and Use, Independent Model Validation, and Model Risk Mitigants. The overarching aim is to bolster policies, procedures, and practices to manage and mitigate risks associated with model usage.

The principles are supported by sub-principles, which include: a proportionate implementation within firms (across models) and across firms and the identification and allocation of the responsibility for the overall MRM framework to the most appropriate senior management function holder.

The impact of these regulations extends beyond internal compliance, influencing financial reporting and the responsibilities of external auditors. Banks are now required to regularly report on the effectiveness of their MRM practices to their audit committees, a measure that underscores the critical role of MRM in financial reporting integrity.

Challenges are anticipated as banks transition from basic tools like spreadsheets to more sophisticated MRM systems. This transition, albeit costly, is necessary for meeting the PRA’s rigorous standards and for the future expansion of internal model approvals, including those involving AI/ML models and climate risk assessments.

Looking ahead

Recognizing the growing usage of AI/ML models, the PRA has worked to future-proof the principles with regard to more advanced modelling methods. This is a helpful addition, since businesses are looking to regulators for direction on how to make sure that AI and ML are implemented in the banking sector in a responsible and legal manner. Going forward, there is expected to be a notable increase in scope of governance activities involving internal and external validation and audit tasks as well as the technology-enablement and framework-led governance for data management, audits, documentation, workflows and so on.

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Author

Prashanth Belugali N

Principal Product Manager

Prashanth has two decades of experience working with large banks, asset managers, trading & capital markets models and model risk domain. He has worked as a quantitative analyst, delivery manager, and product engineer, and provided bespoke solutions in quants (asset management, trading) and risk management practices (credit risk, market risk, model risk), and data engineering to a global clientele