Supervision of banks, insurance companies and pension funds requires access to individual firm data, known as micro data. In Europe, the standard for exchanging supervisory micro data is the eXtended Business Reporting Language (XBRL), whereas the standard for exchanging usually more aggregated statistical data is Statistical Data and Metadata eXchange (SDMX). Respecting the relevant Chinese walls between statistics and supervision, there is an increasing need to combine these data, requiring a mapping between the two standards.
In Europe, the European Banking Authority (EBA), in charge of defining supervisory reporting, is responsible for the Implementing Technical Standard (EBA ITS) and the resulting taxonomy in XBRL, specifying the reporting by banks. The taxonomy is based on a multidimensional Data Point Model.31
From a high-level perspective, both statistical and supervisory standards aim at describing data through concepts represented by lists of codes. However, mapping between those standards depends heavily on the specific implementations. The mapping discussion focuses on mapping the supervisory data content or “facts” to the statistical representation of time series data based on Data Structure Definitions (DSDs). In terms of mapping direction, the mapping from XBRL to SDMX seems more relevant as the SDMX data models focus more than XBRL on characteristics relevant for data processing and analysis.
Mapping between such standards depends on several factors. First, the information worded in both standards should have a well defined and unambiguous relationship. The use of standardised data dictionaries and shared codelists or uniquely mappable equivalents greatly facilitates one-to-one mapping of data standards. Alternatively, calculation rules may specify mappings.
The initiatives of the Joint Expert Group on Reconciliation of credit institutions’ statistical and supervisory reporting requirements (JEGR) and the Groupe de Réflexion on the Integration of Statistical and Supervisory Data (GRISS) aim at defining the relationships between the monetary and financial statistical data definitions used for instance for the Balance Sheet Item (BSI) statistics and the supervisory definitions that form the basis of the EBA ITS. For example, loans defined in BSI are currently not equal to loans according to the EBA ITS. The difference between the two definitions is being identified and will probably be included in the reporting. Furthermore, as statistical data definitions usually address data that are more highly aggregated than for supervisory definitions, clear aggregation rules need to be established.
Second, the standards should be rich enough or extended to word information and functionality available in statistical and/or supervisory data exchange formats. SDMX and XBRL contain different information objects, and work remains to be done on checking their correspondence. XBRL allows the data to be presented according to the original reporting templates and the definition of validation rules. SDMX
31 www.eba.europa.eu/regulation-and-policy/supervisory-reporting/implementing-technical- standard-on-supervisory-reporting-data-point-model-.
provides web services and registries for querying data and metadata and provides metadata attributes at the observation level.
Third, the data modelling objectives for statistical and supervisory data are different. Modelling data for statistical purposes is driven by the need to process and analyse data according to characteristics relevant for such analysis, while ensuring harmonised data definitions and the ability to uniquely define data. The supervisory modelling of data focuses primarily on providing all characteristics required for data harmonisation (usually within a reporting template as defined in the EBA ITS) and for uniquely defining data, and much less on needs related to data analysis. The different approaches in data modelling pose an additional challenge in identifying and extracting those XBRL elements relevant for processing and analysing the data in an SDMX context.
Fourth, a major difference between statistical and supervisory data modelling is that statisticians use DSDs covering data of coherent nature (eg exchange rates), whereby each data element is described by all characteristics (dimensions) in the DSD for exchange rate data. In contrast, the EBA taxonomy for supervisory data covers much more varied data, whereby for each data element only a sparse subset of “reporting relevant” data characteristics (dimensions) are selected from among all the data characteristics available in the taxonomy. Only the “relevant” characteristics are reported, and the others are ignored or considered as defaults.
The above examples illustrate the main difficulties in mapping between statistical and supervisory data. Mechanical mapping from supervisory data definitions to statistical data definitions does not seem to be a fruitful approach, as indicated by the experience of one national central bank in Europe. A more feasible approach is based on providing a carefully crafted mapping table and conversion rules for each coherent group of supervisory data. Again, this conclusion only relates to the specific case of mapping the supervisory reporting as defined in the EBA ITS and taxonomy to a statistical data model in SDMX.
Several central banks (for example, those of Italy and Austria) use the so-called “input approach” aiming at collecting at individual transaction level all characteristics relevant for wording data according to both statistical and supervisory purposes. This approach is currently under investigation on a wider scale and in Austria is already being implemented as a part of the AuRep (Austrian Reporting) project.