PRA looking to reduce the severity of future crises with better Model Risk Management

PRA is directing UK banks to improve model and data governance processes through the introduction of new model risk regulation

UK banks have 12 months to act following the publication of the final supervisory statement of the new PRA Model Risk Management (MRM) Principles paper. Failure to follow these principles may lead to significant fines and loss of reputation which could damage new business opportunities.

The Bank of England’s Prudential Regulation Authority (PRA) also highlighted in its consultation paper CP6/22 the fact that UK banks are lagging behind international peers when it comes to ‘effective and robust’ MRM, leaving them open to damaging losses as well as falling foul of the regulator. The PRA states these proposed standards could reduce the probability and severity of future crises in the financial sector.

Actions and recommendations from SAS come as the PRA introduces a supervisory expectation for firms to meet five MRM principles – and in most cases a number of sub-principles - designed to cover all elements of the model lifecycle. These proposed principles set out what the PRA considers to be the core disciplines necessary for a sound framework to manage model risk effectively across all model and risk types.

The PRA proposes that an accountable individual be appointed at the board level, and made responsible for MRM, taking on overall responsibility for the findings, self-assessment and remediation plans. This individual should be tasked with establishing an effective model risk management framework that applies to all models and feeds into the broader risk management of the bank.

The five key principles are:

  • Model identification and model risk classification: Firms have an established definition of a model that sets the scope for MRM, a model inventory, and a risk-based tiering approach to categorise models to help identify and manage model risk.
  • Governance: Firms have strong governance oversight with a board that promotes an MRM culture from the top through setting clear model risk appetite. The board approves the MRM policy and appoints an accountable individual to assume the responsibility to implement a sound MRM framework that will ensure effective MRM practices.
  • Model development, implementation and use: Firms have a robust model development process with standards for model design and implementation, model selection, and model performance measurement. Testing of data, model construct, assumptions, and model outcomes are performed regularly in order to identify, monitor, record, and remediate model limitations and weaknesses.
  • Independent model validation: Firms have a validation process that provides ongoing, independent, and effective challenge to model development and use. The individual or body within a firm responsible for the approval of a model ensures that validation recommendations for remediation or redevelopment are actioned so that models are suitable for their intended purpose.
  • Model risk mitigants: Firms have established policies and procedures for the use of model risk mitigants when models are under-performing and have procedures for the independent review of post-model adjustments.

David Asermely, Global Model Risk Lead at SAS, said: “SAS has partnered with 80+ banks across the globe to implement robust MRM accumulating knowledge and expertise as the domain has evolved.  We welcome the introduction of these new principles from the PRA and believe that they will help to promote the safety and soundness of UK banks. The PRA consultation paper states that they have found evidence of poor MRM including lack of board involvement and understanding. In my opinion, this will adress these concerns and provide an opportunity to modernise responsibly. 

Dr Iain Brown, Head of Data Science at SAS UK & Ireland, added: “Given the rapidly changing environmental and digital landscapes, as well as increasing use of AI and sophisticated modelling techniques, now is undoubtedly the time for firms to adopt a more strategic approach to MRM. At SAS we are already working with a number of firms to ensure that their models are fair, accountable, transparent and explainable. It’s also important that they have robust model governance procedures to minimise potential exposure to model risk.

“Given the fact that inadequate or flawed design and implementation of models could lead to adverse consequences that pose risks to firms’ overall financial stability, particularly given the current economic and geo-political uncertainty, these five principles could not come soon enough for the sector.”

There is also support for these principles from the UK’s financial services regulator, the Financial Conduct Authority, via a new discussion paper on the use of AI and machine learning in banks: DP5/22

Find out more about how AI and analytics can support better model risk management.

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