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Deutsche Börse Group (DBG)

As described in 1.2.7, the focus of the Discussion Paper lies on credit institutions. Companies in the financial industry often have a large number of subsidiaries, which are, depending on their prudential classification, subject to dedicated supervisory reporting. Due to efficiency and consistency reasons, typically central departments are responsible for preparing and submitting the respective supervisory reports for the differently licensed companies of one group. To avoid potentially jeopardizing the target of streamlining the reporting process and increase efficiencies going forward EBA should broaden the scope of the feasibility study to include, for example, investment firms, financial holding companies, central counterparties, central securities depositories as well as any other prudentially supervised entities. Moreover, we would like to emphasise, that EBA should particularly consider, that entities, classifying as credit institutions or investment firms, often fall under a set of different prudential requirements, resulting from different European as well as national legislations.
We would welcome EBA considering at least the different European legislation simultaneously applicable to companies of the financial industry, e.g. reporting requirements resulting not only from CRR and IFR but also Directive 2014/59/EU (“BRRD”), Directive 2014/65/EU (“MiFiD”), Regulation (EU) No. 600/2014 (”MiFiR”), Regulation (EU) 909/2014 (“CSDR”) and Regulation (EU) 648/2012 (“EMIR”). Otherwise, instead of streamlining the reporting process and increase efficiencies an integrated reporting approach only for a limited scope might increase fragmentation and complexity.
All data collections (prudential/resolution/statistical) should be considered to get the best possible overview and reliable feedback. Moreover, a broad scope of European prudential legislations should be covered by the feasibility study (s. our answer to Q1).
Yes, considered complete and relevant.
We support the open approach of the discussion paper. However, it should be pointed out which relevant regulatory bases are being considered. In addition to being classified as a credit institution, entities can simultaneously also be classified as central counterparty in accordance to Article 14 EMIR institution or CSD in accordance with Article 16 and 54 CSDR.
Not relevantSomewhat relevantRelevantHighly relevant
Training / additional staff (skills)  X 
IT changes  X 
Changes in processes   X
Changes needed in the context of other counterparties / third-party providers  X 
Time required to find new solutions   X
Other (please specify)X   
Highly agreeAgreeSomewhat agreeDon’t agree
Data Dictionary - Semantic levelX   
Data Dictionary - Syntactic levelX   
Data Dictionary - Infrastructure level X  
Data collection - Semantic levelX   
Data collection - Syntactic levelX   
Data collection - Infrastructure level  X 
Data transformation - Semantic levelX   
Data transformation - Syntactic levelX   
Data transformation - Infrastructure levelX   
Data exploration - Semantic levelX   
Data exploration - Syntactic levelX   
Data exploration - Infrastructure levelX   
Data Dictionary - Infrastructure level: Correlation between semantic/syntactic level and Infrastructure level – Integrated implementation of all 3 levels for more efficient spending
Data collection - Infrastructure level: Under estimation of efforts with regard to adjustments in source systems and interfaces -> more flexibility regarding the use of different infrastructure systems considering: heterogeneous IT environments
Data exploration - Infrastructure level: Integration of internal systems to reduce sunk costs
Within DBG, multiple data dictionaries are used.
DBG agrees on the proposed characteristics of the data dictionary. It should be emphasized that the specified data is initially covered at a high level and that updates are provided in a timely manner in the event of extensions. Furthermore, understandable language, simple vocabulary and a central place for data would be preferable.
It creates a common understanding and ensures a high-quality standard.
Understanding reporting regulationX  
Extracting data from internal system X 
Processing data (including data reconciliation before reporting)X  
Exchanging data and monitoring regulators’ feedback X 
Exploring regulatory dataX  
Preparing regulatory disclosure compliance.X  
Other processes of institutions X 
Understanding reporting regulation: Easily accessible; Using simple and clear language and vocabulary to avoid content misunderstandings or misinterpretations
Extracting data from internal system: Reduced internal effort due to consolidated IT system
Processing data (including data reconciliation before reporting): Increases comparability of data due to reconciliation
Exchanging data and monitoring regulators’ feedback: One central environment for the exchange execution
Exploring regulatory data: Standardised rules and regulations
Preparing regulatory disclosure compliance: Content of data dictionary might be used for other processes -> further standardization within groups
Especially with regard to the increasing regulation (and reporting requirements), it is very important for institutions to have a standardised data dictionary available.
Moderately costly
Moderate cost reductions
Moderate cost reductions
DBG agrees with cost-benefits analysis for the data dictionary.
It is reasonable to continue the approach to use more granular data for statistical purposes as already being discussed (1) . With regard to granularity for prudential and resolution data, increased granularity is to be advocated under the premise that a clear responsibility between competent authorities and supervised entities for the aggregation of data is defined.

  • option 1
  • option 2
Challenge: Potential data duplications, creation of common data dictionary.
Possible solution: Clear and complete data dictionary to avoid duplications
Challenge: Potential data duplications, creation of common data dictionary.
Possible solution: Clear and complete data dictionary to avoid duplications
Challenge: Human resources.
Potential solution: Specific training sessions to staff
A central data warehouse, where the collection layer is stored would be on our best advantage.
Highly (1)Medium (2)Low (3)No costs (4)
Collection/compilation of the granular data X  
Additional aggregate calculations due to feedback loops and anchor values  X 
Costs of setting up a common set of transformations* X  
Costs of executing the common set of transformations**  X 
Costs of maintaining a common set of transformations X  
IT resourcesX   
Human resourcesX   
Complexity of the regulatory reporting requirements X  
Data duplication  X 
Other: please specifyX   
Highly (1)Medium (2)Low (3)No benefits (4)
Reducing the number of resubmissionsX   
Less additional national reporting requests X  
Further cross-country harmonisation and standardisationX   
Level playing field in the application of the requirements X  
Simplification of the internal reporting process X  
Reduce data duplications X  
Complexity of the reporting requirements X  
Other: please specifyX   
Increased Human Resources and IT costs could negate the potential benefits.
Authorities and reporting institutions jointly
Harmonised and standardised, ready to be implemented by digital processes (fixed)
Costs: -
Benefits: No room for variations and too much input from companies, increased comparability
Challenges: Increased requests, complexity

Reporting institutions:
Costs: Additional staff costs
Benefits: -
Challenges: -

Jointly authorities and reporting institutions:
Costs: -
Benefits: -
Challenges: Clear definition of responsibilities

Increased level of automation.
Define scope of manual adjustments in a manner that enables to get back to the granular data used.
Set rules how to valuate.
Apply specific concepts in a way that different valuations do not have to be done for every contract.
Check scope for changing framework and see what can be done.
As a first step, all data after the collection process step should be considered as relevant for the feedback loops. In a second step, in collaboration with the NCA’s the data should be defined and readjusted in case of changing demands from the NCA’s.
We receive similar requests from the NCA’s, but different templates. A lack of harmonization can be observed in similar requests, which leads to inefficiencies.
Multiple dictionaries
The dictionaries are grouped by supervisory/financial reporting and statistical reporting
The same format
Very important
Very important, as there is only one central database accessible for everyone. It makes reconciliations easier and faster
• Create consistency
• Comprehensive
• Supportive
System Design Costs Benefits Challenges
Sequential integration Reconciliation and maintenance Independent set up Limited information exchange between authorities
Point-to-point integration Integration costs Shareable data High number of parties involved
Service-bus integration - Central access Inefficient data usage across different data points
Hub-and-spoke integration Implementation costs No duplication, central data register Different solutions (no standardisation)
Centralised system Less flexible for new requirements Harmonisation Removing local systems
Distributed system Implementation costs Harmonisation Transition local to distributed system
Architectures: Stages process for implementation
Yes, to a limited extent
In order to bear the costs of the introduction of integrated reporting, a certain preparatory phase of the financial industry is required. A too fast roll-out could lead to significant costs on the side of the institutions.
not valuable at allvaluable to a degreevaluablehighly valuable
Data definition – Involvement X  
Data definition – Cost contributionX   
Date collection – Involvement X  
Date collection – Cost contribution  X 
Data transformation – Involvement  X 
Data transformation – Cost contribution  X 
Data exploration – Involvement  X 
Data exploration – Cost contribution X  
Data dictionary – Involvement   X
Data dictionary – Cost contribution   X
Granularity – Involvement  X 
Granularity – Cost contribution  X 
Architectures – Involvement   X
Architectures – Cost contribution   X
Governance – InvolvementX   
Governance – Cost contributionX   
Other – Involvement    
Other – Cost contribution    
Data definition: CA should provide clear definitions to avoid potential misinterpretation or diverging understanding by institutions
Data collection: Institutions might need to adapt their systems
Data transformation: Benefit for institutions to have harmonized data
Data exploration: Creates consistency
Data dictionary: Consistency, and comprehension for all institutions
Granularity: Define common understanding for data reported
Architectures: Shows to what extent institutions are able and want to contribute, costs are limited to what institutions are able to provide
Governance: CA need to ensure
1-2% of DBG budget
A mixed (pull and push) approach
Costs: Accesses to internal repository, reorganisation of internal data household
Benefits: Data available all time
Design options/solutions: -
Costs: Increased data integrity, more difficult identification of errors
Benefits: Comparability of values,, detection of changes
Design options/solutions: Technology to detect errors
Costs: More complex due to split responsibilities
Benefits: Suitable approach for certain data, splits responsibility
Design options/solutions: Clear definition for responsibilities
Obstacles/challenges: Outsourcing of some tasks
Possible solution: Adaption to requirements
Obstacles/challenges: Different laws
Possible solution: -
Obstacles/challenges: Ensuring compliance
Possible solution: Creation and definition of master data
Benefits: Ad hoc requests only have to be completed if data is not accessible in CDPC; clear definitions what is needed from the reporting institutions
Governance: Cost-Benefit analysis of needed and/or wanted components
Not fully developed or useful for my needs
In addition costly to implement.
Data transformation
Agree with main obstacles mentioned in the paper. At the moment, no further challenges can be thought of.
via a service provider
Increased automation, less Human Resources to be spent, harmonization of data.
Deutsche Börse Group (DBG)