CEIOPS' Advice for Level 2 Implementing ... - EIOPA - Europa

circumstances of its omission or misstatement. Thus, materiality provides
a threshold or cut-off point rather than being a primary qualitative
characteristic which information must have if it is to be useful”.
3.70 In the context of a valuation of technical provisions, this means that a
misstatement of the technical provision 38 is material if it could influence
the decision-making or judgment of the intended user of the information
contained in the valuation.
3.71 In its calculation of technical provisions, the (re)insurance undertaking
should address materiality consistent with the principle set out in the
above. For this purpose the undertaking should define a concept on
materiality which should lay down the criteria on basis of which a
decision on the materiality of a potential misstatement of technical
provisions is made.
3.72 This materiality concept should be consistent with the undertaking’s
approach to materiality in other areas of solvency assessment (e.g. the
determination of the SCR) and reporting, and should be reflected in the
undertaking’s own risk and solvency assessment (ORSA).
3.73 When determining how to address materiality, the undertaking should
have regard to the purpose of the work and its intended users. For a
valuation of technical provisions – and more generally for a qualitative or
quantitative assessment of risk for solvency purposes – this should
include the supervisory authority which uses the information when
performing the SRP.
Assessment of the estimation uncertainty in the valuation
3.74 Regardless of what methods shall be applied for the valuation of
technical provisions, it is important that an assessment of their
appropriateness should in general include an assessment of the model
error implicit to the calculations.
3.75 Such an assessment may be carried out, for example, by:
• Sensitivity analysis in the framework of the applied model: this means
to vary the parameters and/or the data thereby observing the range
where a best estimate might be located.
• Comparison with the results of other methods: applying different
methods gives insight in potential model errors. These methods would
not necessarily need to be more complex.
• Descriptive statistics: in some cases the applied model allows the
derivation of descriptive statistics on the estimation error contained in
the estimation. 39 Such information may assist in quantitatively
describing the sources of uncertainty.
• Back-testing: comparing the results of the estimation against
experience may help to identify systemic deviations which are due to
deficiencies in the modelling. 40
38 I.e. the degree of model error inherent in the measurement.
39 Of course, this would not include the uncertainty arising from a misspecification of the model itself.
40 Cf. also the third step of the proportionality assessment process.
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© CEIOPS 2010