BCBS 239
Solidatus for BCBS 239
The Basel Committee on Banking Supervision standard number 239 ‘Principles for effective risk data aggregation and risk reporting’ aims to fix risk management failings in systemically important banks. Its Principles are designed to improve the accuracy and speed of risk reporting. While initially focused on risk reporting, the Principles demand data governance and adaptability which can deliver much wider benefits.
Key principles and considerations
The BCBS 239 principles apply to all Global Systemically Important Banks (G-SIBs) and are also recommended to be applied to Domestic Systemically Important Banks (D-SIBs) by national supervisors. Banks are required to be compliant with the Principles and a key component which drives compliance is the documentation of data lineage. The approach to be taken for data lineage has been one of the key challenges that banks have faced in aligning to the Principles as it is one of the more time consuming and resource intensive activities demanded by the regulation. Whilst this is the case, there are strong benefits to implementing a robust and consistent framework, summarized as follows:
Data journey
Provides transparency of definitions of data and an understanding of the manipulation, transformation and aggregation of critical data as it travels through its journey into reporting. Supports a strong understanding of architecture in place.
Data quality (DQ)
Provides clarity on the required data quality framework to be put in place, ensuring that DQ controls and supporting rules are placed in the correct place in the process, in the context of the potential risks and issues that could arise throughout the data journey.
Data governance
Underpins a strong data ownership and governance framework driving consistency, definitional alignment and control across the critical data in scope.
Key benefits
- Administer a common language across functions, while still allowing everyone to speak their own.
- Point-in-time system of record – to be able to show exactly what knowledge was available and when.
- Ability to simulate change within the models – demonstrate to all involved the impact of change based on the facts known at the time.
- Enterprise-scale modeling and visualization – the ability to model complete flows is critical to compliance.
- Reduce risk of non-compliance – by identifying gaps – extent of knowledge is clear and demonstrable.
- Centralized repository, de-centralized collection – always use the subject matter experts to collect and validate the metadata and lineage.
