Data implications of Basel 3.1

Many of the proposed Basel 3.1 rules increase the granularity of data required for firms to mitigate risks, calculate capital requirements, and align internal model risk management with the regulator’s expectations.

This article sets out the main requirements for firms regarding their regulatory reporting and data management approaches under Basel 3.1. For information on the proposed changes to disclosures and regulatory reporting, click here.

What are the main data requirements set out by the PRA for Basel 3.1?

Implications of Due diligence under the Credit Risk Standardised Approach
The proposed rules emphasise a move away from the automatic use of external credit ratings for counterparty credit assessment. Instead, firms must complete due diligence on their counterparties, requiring an understanding of the underlying risk profiles and characteristics associated with them. This will require the collection, storing and interpretation of counterparty data in a way that can be used to identify the appropriate risk weight, and can be easily retrieved to demonstrate to the regulator if unrated corporate exposures are considered investment grade or not.

Increase in Source Data for Credit Risk under the Internal Ratings-Based (IRB) Approach
The proposed rules require a minimum of five years of data for their IRB calculations from at least one source, to estimate all parameters of exposures, except for Loss Given Default (LGD) and Exposure at Default (EAD) for non-retail portfolios where seven years of data is required. Under existing rules, firms could apply for just two years of data for non-retail exposures for up to 5% of their credit risk exposures. This expansion of historical source data means firms will be required to store and manage IRB data for longer time periods.

Output Floor: Data gathering for international Internal Modelling Firms
The proposed rules will apply the output floor to all UK-headquartered groups who have Internal Modelling approval. However, the PRA may request international subsidiaries to provide data on an ad hoc basis to support the PRA’s understanding of the potential impact of application of the floor. Therefore, subsidiaries must also ensure relevant data is readily available should the regulator require it.

To contend with the proposals above, firms should consider the following:

1. Metadata
   Firms should use Metadata to accurately classify assets based on identification factors such as exposure type, counterparty information, risk characteristics, model inputs and historical data. Firms should ensure they add tagging information within their data management systems. Metadata can be used by firms now to help them prepare for Basel 3.1. They should tag assets which will require different treatment according to the new rules, such as a change in risk weight.
2. Data Ownership
   Assigning data ownership to those who utilise it, rather than IT, ensures ownership, completeness, and reliability of data, and aligns with best practice of data governance. By assigning owners to specific datasets which will require different treatment upon the implementation of Basel 3.1, firms can reduce the potential for non-compliance and omittance of key data in the future.
3. Time Cost
   The time-intensive nature of collecting, storing, and managing this data underscores the necessity for a well-established and efficient data governance framework. By beginning a data transformation in preparation for the Basel 3.1 reforms, firms may reduce this time cost in the future.
4. Model Risk Management
   Firms also will be required to think about Basel 3.1 from a Model Risk management perspective. From 17th May 2024 SS1/23 will come into effect. This SS sets out several principles which must be considered by firms that have an internal model approval to calculate regulatory capital requirements. The most applicable from a Basel 3.1 perspective, Principle 3 – Model Development, implementation, and use, requires firms to perform regular testing of their data, construction of models, assumptions, and outcomes to remediate model limitations, risks and weaknesses.
5. Key Takeaway
   As the financial sector begins the transition to the Basel 3.1 regime, the convergence of stringent regulatory requirements and the intricate landscape of data management and governance brings to the forefront the need for adaptive and robust frameworks. Firms should prepare accordingly for this change, ensuring a smooth transition to the new Basel 3.1 rules.

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