Welcome to part 2 of Solvency II Standards for Data Quality – common sense standards for all businesses.
The Solvency II Standards for Data Quality run to 22 pages and provide an excellent substitute to counting sheep if you suffer from insomnia. They are published by The Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS) (now renamed as EIOPA).
I accept that Data Quality Standards cannot aspire to be as page turning as a Dan Brown novel – but plainer English would help.
Anyway – enough complaining. As mentioned in part 1, the standards require insurance companies to provide evidence that their Solvency II submissions are based on data that is “as complete, appropriate, and accurate as possible”. In this post, I will explore what the regulator means by “complete”, “appropriate” and “accurate”. I will look at the terms in the context of data quality for Solvency II, and will highlight how the same common sense standards apply to all organisations.
APPROPRIATE: “Data is considered appropriate if it is suitable for the intended purpose” (page 19, paragraph 3.62).
Insurance companies must ensure they can provide for insurance claims. Hence, to be “appropriate”, the data must relate to the risks covered, and the value of the capital they have to cover potential claims. Insurance industry knowledge is required to identify the “appropriate” data, just as Auto Industry knowledge is required to identify data “appropriate” to the Auto industry etc.
COMPLETE: (This one is pretty heavy, but I will include it verbatim, and then seek to simplify – all comments, contributions and dissenting opinions welcome) (page 19, paragraph 3.64)
“Data is considered to be complete if:
- it allows for the recognition of all the main homogeneous risk groups within the liability portfolio;
- it has sufficient granularity to allow for the identification of trends and to the full understanding of the behaviour of the underlying risks; and
- if sufficient historical information is available.”
As I see it, there must be enough data, at a low enough level of detail, to provide a realistic picture of the main types of risks covered. Enough Historical data is also required, since history of past claims provides a basis for estimating the scale of future claims.
As with the term “Appropriate”, I believe that Insurance industry knowledge is required to identify the data required to ensure that data is “complete”.
ACCURATE: I believe this one is “pure common sense”, and applies to all organisations, across all industries. (page 19, paragraph 3.66)
Data is considered accurate if:
- it is free from material mistakes, errors and omissions;
- the recording of information is adequate, performed in a timely manner and is kept consistent across time;
- a high level of confidence is placed on the data; and
- the undertaking must be able to demonstrate that it recognises the data set as credible by using it throughout the undertakings operations and decision-making processes.
Update – In October 2013, following an 18 month consultative process, DAMA UK published a white paper explaining 6 primary data quality dimensions.
For more details see my blog post, Major step forward in Data Quality Measurement