New Guidance For D-SIBs Adds To The Data Management Burden
Posted: 12 March 2013 | Author: Jeremy Asprey | Source: SecondFloor
The new category of domestic systemically important banks will increase the data management and disclosure burden on qualifying institutions, at a time when many are already feeling the strain.
Banks that escaped classification as G-SIFIs in 2011 may have breathed a sigh of relief at the time, but for many, that relief will have been short-lived. In October last year, the Basel Committee on Banking Supervision (BCBS) set out its proposed framework for an additional category of systemically important financial institutions, so-called D-SIBs, or domestic systemically important banks.
D-SIBs are described by BCBS as “banks that are not significant from an international perspective, but nevertheless could have an important impact on their domestic financial system and economy compared to non-systemic institutions.” (Source: BCBS: A framework for dealing with domestic systemically important banks, October 2012).
A framework based on 12 principles
The BCBS believes that banks that are too big or too important to fail on a national level should be subject to more stringent regulation – particularly in the allocation of capital buffers, but also in the overall quality of governance and risk management – than peers whose failure would have no systemic impact. To this end, it has set out 12 principles that national regulators need to incorporate into a framework for identifying and supervising qualifying banks operating in their jurisdiction; a framework that must be in place by January 2016.
Once identified by their home or host authorities, D-SIBs will be subject to similar capital restrictions to their global counterparts, notably the application of a Higher Loss Absorbency (HLA); an additional capital buffer to those mandated under Basel III.
The aim of the legislation is to prevent significant harm to a real national economy caused by the ‘failure or impairment’ of one or more institutions that are systemically important within the domestic economy, whether through their size, their interconnectedness with other institutions, their uniqueness in terms of the service they provide, or their organisational complexity.
D-SIBs will see their data management burden become heavier still
It’s not yet clear how regulators will go about identifying which banks will qualify as D-SIBs. But one thing is clear: for banks that do qualify (and the list will be reviewed on an annual basis), the new regulatory framework will impose yet another data aggregation and reporting requirement on top an already-onerous compliance workload.
Essentially, D-SIBs will have to demonstrate that they have calculated HLA requirements accurately, and that their governance and risk management activities meet the elevated standards set out in the 12 principles.
Deloitte analyses the likely impact on data governance methods
The impact of this disclosure requirement has been examined in detail by Deloitte in its report Risk, data and the supervisor (October 2012). Deloitte’s report is mainly concerned with G-SIBs, but it seems likely that most of the reporting requirements imposed on G-SIBs will also be imposed on D-SIBs.
Fundamentally, Deloitte notes that the introduction of G-SIBS (and, we can infer, now D-SIBs too), means that data quality and data governance are coming under increased regulatory scrutiny: “The eye of the supervisory community is moving on to data management and away from simply prescribing the data outputs,” it warns.
Elsewhere, the report notes that “implicit in the BCBS’ principles [for G-SIBs] is that underlying data which enables the generation of risk metrics must also be of sufficient quality…this includes counterparty data, legal entity hierarchies, book data, trade data, prices, instrument static, etc.”
Time for a more strategic approach to data management
The conclusion is that banks urgently need to take a more strategic approach to data management, with an emphasis on watertight data governance that spans the entire business and means that any calculation or data point can be validated, reconciled and traced to its source.
That’s not something that can be achieved with ad-hoc projects or manual data gathering processes. It requires an automated, end-to-end, enterprise data governance framework that can be used to gather, calculate, validate and audit data of all kinds, and to turn that data into appropriate reports for all kinds of regulatory disclosure, from D-SIB reporting to Basel III and MiFID, as well as for internal risk analytics and management.