Data is at the very core of the Solvency II articles, and it is clear that any internal model approval process (IMAP) will focus heavily on data input to the model.
It is widely recognized that the increased frequency of Solvency II reporting is likely to require organizations to collect and prepare data faster than they do today.
EIOPA requirements
Many in the industry are finding that they need to aggregate or segment data in new ways, and to source additional data that they have not previously modeled. But most importantly, EIOPA (European Insurance and Occupational Pensions Authority, formerly CEIOPS) has set out a number of explicit and stringent data quality requirements.
EIOPA data quality requirements
For many non-life insurers:
Solvency II data requirements pose significant challenges for their liability data. Catastrophe modeling data is a particular concern, with issues around availability and granularity of key non-financial information.
Specifically, these issues relate to the age of data, as there are potentially long lags between production of useable datasets (exacerbated for reinsurers), and a lack of granularity about geographical location for all the risks and error rates on building types and other descriptor fields.
For life insurers:
There is increasing pressure to obtain greater transparency on asset and investment risk data, with counterparty exposure, corporate debt and liquidity of assets being targeted in particular.
Solvency II QIS
The Solvency II QIS exercises have frequently highlighted to firms a number of additional data requirements to calculate solvency capital under the standard formula, and to populate the reporting templates.
Additional processes and controls are likely to be required for all firms, for example, in the sourcing of additional data for complex assets for investment risk and counterparty risk modeling. The most recent QIS 5 exercise highlighted the granularity of asset data as a key challenge facing the industry.
In many cases, the current data available was found to be insufficient for performing the full look-through calculations for the SCR as required by QIS 5. As a result, a suite of assumptions had to be made on the underlying asset mix, with minor changes to the assumptions, potentially resulting in material changes to model outputs.
Regulators are likely to take a dim view of such assumptions being used in lieu of reliable asset data.
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