Creating a compliant data
Creating a compliant data
management framework:
management framework:
the regulatory perspective
the regulatory perspective
Paolo Cadoni
Paolo Cadoni
EIOPA Internal Model Committee Chair
EIOPA Internal Model Committee Chair
Life & Pension Risk Nordics
Outline
Outline
• The importance of a good data management framework • The “Babel” problem
• Solvency II data quality requirements
o Accuracy
o Completeness o Appropriateness
o Data directory – classification and maintenance of the data Directory o Data policy
o Validation o Documentation o External data
Importance of a good data
Importance of a good data
management framework
management framework
• Data operations are known to be vulnerable to errors
depending on complexity, reliance on manual operations
and/or spreadsheets, when interpretations are required
• Data errors could have a high impact on firms balance sheet
and operations
• It is a myth that data is just moving stuff around…
o nearly always, data is being changed in some way; and o its meaning is changed (or lost)
The Babel problem
The Babel problem
• One of the most difficult issues
facing the financial industry.
• A mass of disparate systems
o Mainframes, SQL databases, flat files, user built spreadsheets
o None of them understand each other.
Solvency II data quality
Solvency II data quality
requirements
requirements
• Data quality requirements do not apply only to internal
models, but also to other areas of the Solvency II
framework. For example:
o Technical provisions
o Undertaking Specific Parameters (USPs) o ORSA
o Etc.
• Emphasis on data governance
o Solvency II will require systematic processes for managing data
(identification, collection, documentation, transmission, processing and retention of data).
Accuracy, completeness and
Accuracy, completeness and
appropriateness
appropriateness
• Accuracy - Data is free from material errors.
• Completeness - Data is of sufficient granularity and available for all
relevant model parameters in case of internal models.
• Appropriateness - Data is consistent with the purpose for which it will be
Data Directory
Data Directory
• A directory of all data used in the calculation of the technical
provisions/internal model is required
• This should cover:
o the source,
o characteristics and
o use of the data in calculations
• Recurrent issues:
o data classification o maintenance
Data policy
Data policy
• A written data policy is required for technical provisions,
internal models and USP.
• Data policy should cover:
o Definition and assessment of the quality of data.
o The use and setting of assumptions made in the collection, processing and application of data.
Validation
Validation
• Undertakings need to validate the calculation of technical
provisions, at least once a year and where there are indications that the data, assumptions or methods used in the calculation or the level of the technical provisions are no longer appropriate.
• The validation process applies to all parts of the internal model (including data)
• The validation process needs to cover, in particular, the:
o appropriateness, completeness and accuracy of data used in the calculations
Documentation
Documentation
• Amongst others, undertakings need to document/evidence
appropriately:
o data directory o data policy
o any material limitations of the data
o the collection of data and analysis of its quality and other information
o the deficiencies in data used in the internal model and the lack of data for the calculation of the internal model
o the risks arising out of the use of external data in the internal model
External data
External data
• Data quality requirements for external data are different for
internal models, technical provisions and undertaking
specific parameters.
o internal models - requirements for external data are same as those that apply to internal data.
o USP - additional requirements to be satisfied on top of those required for technical provisions
In conclusion
In conclusion
…
…
• Anecdotal evidence shows that often data preparations are left until quite late – very difficult to set up and document the
necessary control, ownership of monitoring of data flows..
• How do you approach data management? Have you considered what part of your business require a large and complex data management process?
• Are you able to categorise data appropriately, and apply adequate controls for each type of data?
A few extracts from the
A few extracts from the
Solvency II Directive
Solvency II Directive
• Member States shall ensure that insurance and reinsurance undertakings have internal model processes and procedures in place to ensure the appropriateness, completeness and
accuracy of the data used in the calculation of their technical
provisions (Art. 82)
• Data used for the internal model shall be accurate, complete and
External data requirements for
External data requirements for
technical provisions
technical provisions
• Data available from an internal source is not more suitable than that available from an external source.
• The origins of the data and the assumptions or methodologies used to process that data are known to the undertaking.
• Indentify any trends in the original data and the variation.
• Assumptions and methodologies appropriately reflect the characteristics of the undertaking’s portfolio of (re)insurance obligations.
External data requirements for USP
External data requirements for USP
• Process for collecting data is transparent and auditable.
• Assumptions made in the collection, processing and application of data are sufficiently comparable in case of different sources used to calibrate USP.
• Data do not reflect any risk mitigating effects of reinsurance contracts or arrangements with SPV.
• Undertakings who are contributing to external data have a similar risk profile.
