CEIOPS Advice for Level 2
Implementing Measures on Solvency II:
Technical Provisions – Article 86 f Standards for Data Quality,
October 2009
Extracts from Level 1 Text
Legal basis for implementing measures
Article 86 – Implementing measures
The Commission shall adopt implementing measures laying down the
following: […]
f. the standards to be met with respect to
ensuring the
appropriateness, completeness and accuracy of
the data used in the calculation of technical provisions, and the
specific circumstances in which it
would be appropriate to use approximations, including case-by-case
approaches, to calculate the best
estimate […];
Other relevant
Level 1 text for providing background to the advice
Article 82 - Data quality and
application of approximations, including case-by- case approaches,
for technical provisions
Member States shall ensure that
insurance and reinsurance
undertakings have internal processes and
procedures in place to ensure the appropriateness, completeness
and accuracy of the data used in the
calculation of their technical provisions.
Where, in specific circumstances, insurance and reinsurance
undertakings have insufficient data of
appropriate quality to apply reliable actuarial method to a set or
subset of their insurance and
reinsurance obligations, or amounts recoverable from reinsurance
contracts and special purpose
vehicles, appropriate approximations, including case-by-case
approaches, may be used in the
calculation of the best estimate.
Article 48 lists the responsibilities of the actuarial function,
one of which is “to assess the
sufficiency and quality of the data used in the calculation of
technical provisions”.
In Article 76(3), reference is made to the need to value technical
provisions consistent with
“information provided by the financial markets and generally
available data on insurance and
reinsurance technical risks (market consistency)”.
Similarly, Article 77(2) stipulates that the calculation of the
best estimate shall be “based upon
up-to-date and credible information (…) and be performed using
adequate actuarial and statistical
methods”.
Article 84 also refers that, upon request of the supervisory
authority, insurers shall to be able to
demonstrate “the adequacy of the underlying statistical data used”
for the application of the
estimation methods for technical provisions.
2.1 It is noted that the Level 1 text also includes considerations
on the issue of data quality in
other contexts not immediately relevant to the valuation of
technical provisions.
In particular, this
is the case with regard to:
• The use of undertaking-specific parameters in the SCR standard
formula; and
• The statistical quality standards and validation standards
applicable for the use of internal
models.
2.2 In order to ensure a consistent approach,
the definition of
the three criteria used in the
assessment of data quality – appropriateness, completeness and
accuracy – presented in this paper is,
to the extent appropriate, also applicable in an analogous manner
to such particular contexts.
However, considering that the scope, the level of demand and the
objectives are different, the
concrete application of the criteria to the particularities of
each context is being further developed
in other relevant
CEIOPS’ advice.
3. Advice
3.1 Explanatory text
3.1 Considering the call for implementing measure in Article 86
(f) the purpose of this paper is to
consider:
• how the main criteria for an assessment of the quality of data –
appropriateness, completeness and
accuracy - should be interpreted in the context of the valuation
of technical provisions;
• which internal processes and procedures would need to be
implemented to ensure that the data used in
the valuation of technical provisions complies with these quality
criteria;
• how the quality of the data used in the calculation of technical
provisions could be reviewed and
validated and by whom such review should be carried out;
• the circumstances under which data deficiencies could arise, and
expectations towards the
undertaking in such cases.
3.2 When considering the issues mentioned in the previous
paragraph, this paper focuses on
requirements relating to the collection, storage and processing of
the data by the insurer as a first
step in the valuation process.
3.3 However, it should be noted that the quality of the data used
in the determination of technical
provisions is also an essential aspect during the provisioning
analysis itself, where the insurer has
to select and assess the data used in the valuation.
Therefore,
this paper also discusses the data quality issues in this particular context.
3.1.1 Criteria to assess the quality of data
3.4 From Article 82 of the Level 1 text it can be inferred that,
the quality of data should be
assessed by scrutinising a set of three criteria: appropriateness,
completeness and accuracy.
Thus, as a general principle, the valuation of technical
provisions should be based on data which is considered complete, accurate and appropriate for that purpose.
3.5 The assessment of the quality of data – in particular, the
criteria of appropriateness and
completeness – should in principle be done at the portfolio level,
and where relevant at a more
granular level, including if necessary the analysis relating to
the individual items.
The assessment shall take into account the set of available data
which is necessary and relevant to
carry out the intended analysis. This includes both internal and
external information to the
undertaking.
The assessment of the accuracy criteria should be
carried out at a more granular level,
relating to the individual items.
3.6 In particular, this applies when a set of data is used to set
a particular assumption.
The set of data used for this purpose should be checked for
verification of the three criteria, as to
ensure that the assumptions used in the valuation of technical
provisions are as much as possible
adequate, up-to-date, prospective, realistic and credible.
3.7 The following paragraphs discuss how each of these three
criteria should be interpreted when
assessing the quality of data.
Appropriateness
3.8 Data is considered to be appropriate if it is
suitable for the
intended purpose (e.g. the
valuation of technical provisions, setting of assumptions) and
relevant to the portfolio of risks
being analysed (i.e. directly relates to the underlying risk
drivers).
3.9 In particular, to be appropriate for valuation purposes the
data needs to be representative of the
portfolio of liabilities being valued and suitable to be used for
an estimation of future cash flows
(consistent with a prospective view on the behaviour of the
relevant risks).
Completeness
3.10 Data is considered to be complete if it allows for the
recognition of all the main homogeneous
risk groups within the insurance or reinsurance portfolio.
It should be noted that Article 80 implicitly implies that the
calculation of technical provisions
shall be done at the level of homogeneous risk groups.
3.11 Thus, data is considered to be complete if it has sufficient
granularity to allow for the
identification of trends and the full understanding of the
behaviour of the underlying risks.
The detail of information collected should be such that it allows
for the application of adequate
provisioning methodologies.
Moreover, data is considered complete if sufficient historical
information is available (e.g. the
run-off triangle is of a sufficiently large size compared to the
number of years within is
considered reasonable that all claims are paid and closed).
3.12 In principle, the more heterogeneous the portfolio is, the
more detailed the data should be.
Where data is complete, it would generally allow for the
application of a reliable actuarial method
for the valuation of technical provisions.
3.13 All material information shall be taken into account and
reflected in the
data set.
No relevant items shall be omitted in the process of
data
collection as this would distort the image of the undertaking’s
activity.
In
case of a lack of information, data can be considered as complete
only if
such deficiency can be justified as immaterial.
The assessment
should also
include an analysis of whether the undertaking’s information is
comprehensive and a relative comparison with other data for
similar lines
of business and/or risk factors.
Accuracy
3.14 Data is considered to be accurate if it is
free from material
mistakes,
errors and omissions.
Most of these will be caused
by human error
or IT
failures, thus a particular link exists with
operational risk, in
particular the
systems and processes employed by the company.
An additional
exposure
to errors may stem from data and system architecture weaknesses, such as:
several
different data systems are being used, the interface between such
systems is not fully automated, the data systems are outdated
and/or there is not a general policy to link the design of data
systems with the technical areas of the company.
Furthermore, the sales
channel and
the outsourcing of services is also important, as the undertaking
may lose
certain control over the data collection process if the products
are sold or
managed via intermediaries.
3.15 Moreover, data is considered to be accurate if the recording
of information
is adequate, performed in a timely manner and is kept consistent
over
time.
This is particularly applicable to certain information which
may be
obvious – for instance, recording information on the claims date
for latent
claims may be particularly challenging, but what would be of
particular
importance is to define and document an adequate policy to deal
with such situations in a consistent manner.
3.16 Accuracy means that a high level of confidence can be placed
on the data.
The undertaking must be able to demonstrate that it recognises the
data
set as credible by using it throughout the undertaking’s
operations and
decision-making processes.
The assessment of the accuracy criteria
should
include appropriate cross-checks and internal tests to the
consistency of
data (i.e. with other relevant information or with the same data
in
different points in time).
3.1.2 Data deficiencies
3.17 Frequently the data available to the undertaking may not be
fully appropriate, accurate and
complete.
There are broad reasons why such data deficiencies
may occur:
• Reasons related to the nature or size of the portfolio; and
• Reasons related to deficiencies in the undertakings’ internal
processes of collecting, storing or
validating data quality.
• Reasons related to deficiencies in the exchange of information
with business partners in a reliable
and standardized way
3.18 The following are examples for reasons related to the nature
or size of the portfolio:
• The frequency of claims may be low, leading to a slow building
process of the database;
• The extent to which historical claims data is available may be
insufficient (e.g. in the case of a
new insurance company or a new line of business);
• The quantity of data may be limited because the volume of
business is small;
• Legal or other fundamental external or internal changes in the
operating environment may reduce the
adequacy of the historical data in predicting future behaviour.
• The claims data are not sufficiently homogeneous to determine
claims patterns on the basis of which
a reliable estimate could be derived.
3.19 Deficiencies in the undertaking’s internal processes could
stem, for instance, from IT mistakes,
high cost of collecting or maintaining existent data, or a
misinterpretation of what is necessary in
achieving an appropriate valuation.
3.20 Where the undertaking has only insufficient own data of
appropriate quality available for the
valuation of technical provisions, it should assess why this is
the case and, subject to
proportionality, which steps it could take to increase the quality
and quantity of its data.
3.21 In particular, the undertaking should assess:
• Whether the lack of data is related to deficiencies in the
internal processes;
• whether the lack of data is related to deficiencies in the data
transmission process with third
parties (including related entities);
• Whether any external data supplied by third parties or market
data could be used;
• Whether the quality of the available data could be enhanced.
3.22 Where the data deficiency is related to insufficient internal
processes, the undertaking should,
subject to proportionality, take appropriate measures to remedy in
due course this situation and to
ensure the adequacy of internal processes and procedures for
collecting, storing and validating of
data used for the valuation of technical provisions.
3.23 To enhance the quality of its data, it may be appropriate for
the undertaking to apply
adjustments to its data.
For example,
changes in the operating
environment (e.g. changes in
legislation) may reduce the appropriateness of the historical
data, because it becomes less credible
for prediction exercises.
In these cases it may be possible to
enhance the quality of the data by
reasonably adapting the historical data to the new reality, for
instance by means of adjustments to
the quantitative data and/or by complementing it with expert
opinion (see CEIOPS-DOC-33/09
advice actuarial and statistical methodologies to calculate the
best estimate).
3.24 However, any such adjustment and the underlying assumptions
should be carefully justified and
documented, and should not overwrite the raw data.
3.25 In circumstances where (e.g. due to the nature or size of the
portfolio) a lack of data for the
valuation of technical provisions is unavoidable for the
undertaking, insurers may have to use
“appropriate approximations, including case by case approaches”
(Article 82).
In such cases, further judgmental adjustments or assumptions to
the data may often need to be
applied in order to allow the valuation to be performed using such
approximations in line with the
principle of proportionality.
The use of expert judgement and the assumptions applied for this
purpose, shall meet the requirements
set out in CEIOPS-DOC-33/09.
3.26 However, in no case should the use of approximations be seen
as an alternative to implementing
appropriate systems and processes for collecting material relevant
information and building historical
databases.
3.1.3 Application of the principle of proportionality
3.27 The degree of appropriateness, completeness and accuracy of
data expected from the insurer should
be consistent with the principle of proportionality and with the
purpose of the analysis.
3.28 In practice, as the requirements should be seen in relation
to the intended purpose of the
analysis/valuation, for portfolios whose underlying risks are
considered simple in terms of nature,
scale and complexity, “appropriate” would automatically be
interpreted differently than in a situation
where there are complex risks (since it would be expected that
less data is needed to evaluate simple
risks).
3.29 However, this should not work as a justification to lower the
general standards for the
collection of data procedures and on the efforts to ensure its
appropriateness, completeness and,
especially, accuracy.
It should be noted that past data may become relevant in the
future if the way in which the principle
of proportionality applies for that line of business changes in
the future.
3.30 On the other hand, proportionality should apply
symmetrically, i.e. where the nature, scale and
complexity of the underlying risks is high, companies should pay
increased attention to the standards
and requirements regarding data quality management.
3.31 However, in certain circumstances there may be a clash
between the amount of information available in practice and the
principle of proportionality.
For instance,
complex risks may have a relatively low
frequency (e.g. aviation, catastrophes, etc.), thus leading to a
very slow process of collecting claims information.
The relative amount of
claims information seems to be at
odds with the proportionality principle.
In such cases, the process of collecting, storing and validating
information should still be robust,
but the company would be required to complement it by making extra
efforts to look for relevant
external information to allow the understanding of the underlying
risks and to use extensively
adequate expert opinion and judgements.
Documentation is also a key aspect in this subject.
3.1.4 Requirements on internal processes and procedures
3.32 In order to ensure on a continuous basis a sufficient quality
of the data used in the valuation
of technical provisions, the undertaking should have in place
internal systems and procedures covering
the following areas:
• Data quality management;
• Internal processes on the identification, collection, and
processing of data; and
• The role of internal/external auditors and the actuarial
function.
3.1.4.1 Data quality management - Internal processes
3.33 Data quality management is a continuous process
that should
comprise the following steps:
a) Definition of the data;
b) Assessment of the quality of data;
c) Resolution of the material problems identified;
d) Monitoring data quality.
3.34 Definition of the data comprises the identification of the
needs in terms of data, a detailed
description of the items that should be collected and the eventual
relations between the different
items.
When performing a provisioning analysis, this step represents the
starting point for the IT
extractions, and the eventual calculations.
In case of an inaccurate data description, the interpretation of
the requirement could be too wide and
then would imply errors.
A comprehensive list of the data required by the provisioning
process should be maintained.
This would include specification of segmentation by
homogenous
risk groups and any additional split of
the data required.
3.35 The assessment of the quality of data implies the
verification of the features that data must
possess in order to be able to produce credible estimates of
technical provisions, i.e. the
verification of the criteria of appropriateness, completeness and
accuracy for the purpose of the
analysis.
Although such assessment may make use of adequate objective
measures and indicators, it should also be
subject to judgement.
3.36 The assessment of data quality should have due regard to the
quality and performance of the
channels used to collect, store, process and transmit data, in
particular when data is provided by
third parties (e.g. intermediaries) or through electronic sources
(e.g. internet).
3.37 If material problems with the verification of the data
quality criteria have been identified, the
insurer should try to solve them within an appropriate timeframe
(to the extent possible, but while
keeping track of the raw data) and should work towards the
improvement of the data collection, storage
or other relevant internal processes, so as to ensure the quality
of the future data.
Those data limitations should be appropriately documented,
including a description of how such
situations can be remedied and the assignment of responsibilities
within the undertaking.
3.38 Finally, data quality should be monitored periodically, with
due regard to the principle of
proportionality. This involves, in particular, the monitoring of
the performance of the relevant IT
systems and of the channels used to collect, store, transmit and
process data.
This process could be based, namely, on data quality performance
indicators, but expert judgement
needs to play a key role in the analysis.
3.1.4.2 Identification, collection and processing of the data
3.39 Identification, collection and processing of the data are
steps required to perform the
calculation of technical provisions.
Hereunder,
the main
principles that should be followed in these
processes are being listed.
• Data should be registered and maintained on a comprehensive
basis and the underlying processes and
procedures should be transparent;
• Data collected should be sufficiently granular in order to apply
adequate provisioning methodologies
and generate results with a sufficient level of detail and
robustness;
• Where it remains useful for the purpose of valuing technical
provisions, historical data should
generally be kept and its availability should increase over time
(e.g. for instance, this would
not happen if valuable data from the older accident years is
automatically ignored or truncated);
• Any adjustments to the original data must be documented as well
as its reasons, in particular the
correction of any data errors and omissions, and the original
database should be maintained;
• Data quality assessments should be made periodically and, once
the results are obtained, corrections
may take place in the form of suitable quantitative or qualitative
changes.
3.1.4.3 Role of external auditor and actuarial function
3.40 Generally speaking, the role of both the external auditors
and the actuarial function requires
that some degree of analysis is performed with regard to the
quality of the data, although the focus,
the objectives and the techniques employed for such an assessment
will be different.
3.41 External auditors will be required to audit specific sets of
data, i.e. to conduct a formal and
systematic examination for the purpose of testing its accuracy,
using techniques commonly employed by
audit professionals.
3.42 On the other hand, the actuarial function will be required to
‘review’ the quality of data, more
specifically, to perform examinations of the characteristics of
the selected data to determine if such
data appear to be reasonable and consistent for the purposes of
the analysis (note that review is not
an audit of data).
3.43 In the calculation of technical provisions,
actuarial
expertise presents an important role in
selection of data to be included.
A more detailed description of the role of the external auditor
and actuarial function is out of the
scope of this paper. Interested parties should refer to CEIOPS-DOC-
29/09 advice on the system of
governance.
3.1.5 Issues of data quality in the context of a provisioning
analysis and review
3.44 As has already been observed, data quality issues are also
important in the context of a
valuation analysis and review carried out by the actuarial
function.
3.45 This would for example include:
• The selection of data to be used for the valuation;
• A review of the appropriateness of the data, having regard to
the three criteria (appropriateness,
completeness and accuracy) as described above and the specific
valuation methodology to be applied;
• An assessment on whether additional external data would be
needed or whether enhancements to the
available data should be sought;
• An assessment whether any adjustments may need to be applied to
the available data, as part of
actuarial best practice, to improve the goodness-of-fit and the
reliability of the estimates derived
from actuarial and statistical provisioning methodologies.
3.46 In such a specific context, the assessment of data quality
for the purpose of the analysis would
necessarily be more granular, as it would be made with a view to
fit a specific methodology or to
review the appropriateness of specific assumptions and parameters.
3.47 The requirements to set up adequate internal processes and
procedures, in the context of Article
82, should not relate to such a granular level, but it should
consider data quality from an overall
perspective for the purposes of calculation of technical
provisions, without necessarily relating it
to the application of particular methodologies.
3.48 It is noted that data quality issues in the context of a
valuation analysis and review (including
any adjustments made by the actuarial function as part of the
provisioning process) in relation to the
quality of data vis-à-vis particular methodologies, are also
relevant in the context of the call for
implementing measure stipulated in Article 86(a)13.
Moreover, such data quality issues are related to two of the
responsibilities of the actuarial
function: “to ensure the appropriateness of the methodologies and
underlying models used as well as
the assumptions made in the calculation of technical provisions”
(Article 48(b)) and “to assess the
sufficiency and quality of the data used in the calculation of
technical provisions” (Article 48(c)).
3.49 As a general principle, the actuarial function should judge
how much credibility should be
assigned to historical data and to prospective assumptions.
This judgement has to be based, namely, on a careful analysis of
the underlying liabilities, the
company and portfolio’s experience and relevant qualitative
information.
3.50 In particular, to fulfil the criteria of the appropriateness
of data, the analysis of the source
and impact of unusual observations is necessary, in order to
decide which weights should be assigned
to these observations.
Sometimes these observations should be treated as outliers but in
other cases they are the effect of
the randomness of the process (bad/good luck) and therefore
indicate the hidden nature of the process
and, for this reason, should be duly considered and documented.
3.1.5.1 Circumstances where adjustments to historical data may be
needed in the context of the
provisioning analysis
3.51 When applying provisioning methodologies, the actuarial
function may need to introduce
adjustments to the historical data, not because the data is
considered inaccurate, but because it is
necessary to increase its credibility and to better align it with
the characteristics of the (sub-)
portfolio being valued and with the future expected behaviour of
risks.
The following is a non-exhaustive list of situations that are
likely to require adjustments to the
historical data, specifically when the best estimate is calculated
from the projection of run-off
triangles:
• unusually heavy or light experience in a given period;
• reflection of claims cycles;
• reflection of future expected trends;
• reflection of changes in risk, for instance due to a one-off
change in the operating environment
(e.g. court award increasing the costs of a particular type of
claims);
• reflection of changes in cover (e.g. company may decide to
introduce/change/remove an excess in its
policies, and the past claims data reflects a different reality in
policy covers);
• reflection of changes in the reinsurance policies;
• occurrence of large or exceptional claims;
• lower the credibility of older data, because the further back we
go, the less relevant and
appropriate the data may be;
• create statistical mass sufficient to extract statistically
credible conclusions by pooling more
than one homogeneous risk group.
3.1.5.2 Issues related to external data or market benchmarks
3.52 In the context of the provisioning analysis, it may be
necessary to complement the internal data
available with external data supplied by third parties or market
data.
This will be the case, for instance, for inflation indices and
other information that effectively
contributes to the understanding of the risks underlying the
liability portfolio and to the
setting of realistic and credible assumptions.
3.53 As mentioned in paragraph 3.5, when assessing the general
requirements on data quality –
appropriateness, completeness and accuracy –this external and
market information should be part of the
analysis.
3.54 In the particular case of external and market information,
the verification of the three criteria
implies:
• Appropriateness and completeness: the assessment of these
criteria is performed at the portfolio
level, considering the set of available data necessary to fully
carry out the intended analysis
(in particular, when setting one particular assumption).
Where relevant, the assessment of appropriateness and completeness
shall also be performed at a more
granular level, including if necessary the analysis relating to
the individual items.
Undertakings are expected to verify that the inclusion of the
individual items of external and market
information contribute towards the enhancement of the
appropriateness and completeness criteria having
regard to the intended purpose of the analysis;
• Accuracy: as individual items of external and market information
have not been collected and
compiled by the undertaking itself, the assessment of its accuracy
is likely to be challenging.
The
verification of this criterion will have to consider the
reliability of the sources of information and
the consistency and stability of its process of collecting and
publishing information across time.
3.55 Moreover, whenever adequate, measurement of the quality and
credibility of the available data in
the context of provisioning analysis should have regard to
available industry or market data which is
deemed comparable, having regard in particular to the requirements
set in Article 76(3).
Any material deviations should be identified and interpreted, for
instance by referring to the
specificities of the own portfolio being valued.
3.2 CEIOPS’ advice
Definition of the term ‘data’
3.56 For the purposes of this advice, ‘data’ refers to all the
information which is directly or
indirectly needed in order to carry out a valuation of technical
provisions, in particular enabling
the use of appropriate actuarial and statistical methodologies, in
line with the underlying
(re)insurance obligations, undertaking’s specificities and with
the principle of proportionality.
Moreover, data comprises numerical, census or classification
information but not qualitative
information.
Assumptions are not regarded as data, but it is noted that the use
of data is an important basis in
the development of actuarial assumptions.
General requirements on data quality in the context of valuing
technical provisions
3.57 As a general principle, undertakings should make all efforts
to ensure that the data available
for the valuation of technical provisions is as appropriate,
complete and accurate for that purpose as
possible.
3.58 Undertakings should assess and monitor the quality of the
data used in the valuation of their
technical provisions.
An assessment of the quality of data should
be carried out on basis of three
criteria: appropriateness, completeness and accuracy.
This also applies to data used to set a particular assumption, as
to ensure that the assumptions used
in the valuation of technical provisions are as much as possible
adequate, up-to-date, prospective,
realistic and credible.
3.59 In order to ensure the appropriateness, completeness and
accuracy of the data used in the
valuation of technical provisions, undertakings should have in
place adequate internal processes and
procedures.
These processes and procedures shall cover the undertakings’
systems used for data quality management
and for the collection, storing and processing of the data.
3.60 In the context of the calculation of technical provisions,
the degree of appropriateness,
completeness and accuracy of data expected from the insurer should
be consistent with the principle of
proportionality, as with the other requirements set out in the
present advice.
However, the application of such principle should not lead to a
lowering of the general standards for
the collection of data procedures and on the efforts to ensure its
appropriateness, completeness and,
especially, accuracy.
Appropriateness, completeness and accuracy of data
3.61 The assessment of the quality of data used in the calculation
of technical provisions – in
particular, the criteria of appropriateness and completeness –
should in principle be done at the
portfolio level, and where relevant at a more granular level,
including if necessary the analysis
relating to the individual items.
The assessment shall take into account the set of available data
which is necessary and relevant to
carry out the intended analysis.
This includes both internal and external information to the
undertaking.
On the other hand, the assessment of the accuracy criteria should
consider a more granular level,
relating to the individual items.
3.62 Data is considered appropriate if:
• it is suitable for the intended purpose (i.e. the valuation of
technical provisions, setting of
assumptions); and
• relevant to the portfolio of risks being analysed (i.e. directly
relates to the underlying risk
drivers).
3.63 Hence, to be appropriate for valuation purposes the data
needs to be representative of the
portfolio of liabilities being valued and suitable to be used to
estimate the future in- and out-going
cash flows from the liabilities (consistent with a prospective
view on the behaviour of the relevant
risks).
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.
3.65 The assessment of the completeness criteria should include an
analysis of whether the
undertaking’s information is comprehensive and a relative
comparison with other data for similar lines
of business and/or risk factors.
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.
3.67 The assessment of the accuracy criteria should include
appropriate crosschecks and internal tests
to the consistency of data (i.e. with other relevant information
or with the same data in different
points in time).
3.68 The combination of accuracy, completeness and appropriateness
of information collected should be
such that it allows for the application of adequate provisioning
methodologies.
Data deficiencies
3.69 Where the undertaking has only insufficient own data of
appropriate quality available for the
valuation of technical provisions, it should assess why this is
the case and which options would be
available to him to increase the quality and quantity of its data.
3.70 In particular, the undertaking should assess:
• whether the lack of data is related to deficiencies in the
internal processes;
• whether the lack of data is related to deficiencies in the data
transmission process with third
parties (including related entities);
• whether the quality of the available data could be enhanced.
• whether any external data supplied by third parties or market
data could be used;
3.71 Where the data deficiency is related to insufficient internal
processes, the undertaking should
take appropriate measures to remedy this situation in due course
and to ensure the adequacy of
internal processes and procedures for collecting, storing and
validating of data used in the
valuation of technical provisions.
3.72 To enhance the quality of its data,
it may be appropriate for
the undertaking to apply
adjustments to its data (e.g. to adapt historical data in case of
changes in the operating environment
or changes in legislation).
These adjustments and the underlying assumptions should be
carefully justified and documented, and
should not overwrite the raw data.
3.73 In circumstances where (e.g. due to the nature or size of the
portfolio) a lack of data for the
valuation of technical provisions is unavoidable for the
undertaking, further judgmental adjustments
or assumptions to the data may need to be applied in order to
allow the valuation to be performed
(using appropriate approximations).
The use of expert judgement and the assumptions applied for this
purpose, shall meet the requirements
set out in CEIOPS-DOC-33/09 advice on actuarial and statistical
methodologies to calculate the best
estimate.
3.74 However, in no case should the use of approximations be seen
as an alternative to implementing
appropriate systems and processes for collecting material relevant
information and building historical
databases.
Systems of data quality management
3.75 Data quality management is a continuous process that should
comprise the following steps:
• Definition of the data;
• Assessment of the quality of data;
• Resolution of the material problems identified;
• Monitoring data quality.
3.76 Definition of the data comprises the identification of the
needs in terms of data, a detailed
description of the items that should be collected and the eventual
relations between the different
items.
3.77 The assessment of the quality of data implies the
verification of the features that data must
possess in order to be able to produce credible estimates of
technical provisions, i.e. the
verification of the criteria of appropriateness, completeness and
accuracy for the purpose of the
analysis.
Although such assessment may make use of adequate objective
measures and indicators, it should also be
subject to judgement.
3.78 The assessment of data quality should have due regard to the
quality and performance of the
channels used to collect, store, transmit and process data, in
particular when data is provided by
third parties (e.g. intermediaries) or through electronic sources
(e.g. internet).
3.79 If material problems with the verification of the data
quality criteria have been identified, the
insurer should try to solve them within an appropriate timeframe
(to the extent possible, but while
keeping track of the raw data) and should work towards the
improvement of the data collection, storage
or other relevant internal processes, so as to ensure the quality
of the future data.
Those data limitations should be appropriately documented,
including a description of how such
situations can be remedied and the assignment of responsibilities
within the undertaking.
3.80 Data quality should be monitored periodically, with due
regard to the principle of
proportionality.
This involves, in particular, the monitoring of
the performance of the relevant IT
systems and of the channels used to collect, store, transmit and
process data.
This process could be based, namely, on data quality performance
indicators, but expert judgement
needs to play a key role in the analysis.
Collection, storing and processing of data
3.81 Data should be registered and maintained on a comprehensive
basis and the underlying processes
and procedures should be transparent.
3.82 Data collected should be sufficiently granular in order to
apply adequate provisioning
methodologies and generate results with a sufficient level of
detail and robustness.
3.83 Where it remains useful for the purpose of valuing technical
provisions, historical data should
generally be kept and its availability should increase over time.
3.84 Any adjustments to the original data must be documented as
well as its reasons, in particular the
correction of any data errors and omissions, and the original
database should be maintained.
3.85 Data quality assessments should be made periodically and,
once the results have been obtained,
corrections may take place in the form of suitable quantitative or
qualitative changes.
Issues of data quality in the context of a provisioning analysis
and review
3.86 Adjustments to the available data may be necessary in order
to improve the reliability of the
estimates derived from actuarial and statistical provisioning
methodologies.
In such a specific context, the assessment of data quality for the
purpose of the analysis would
necessarily be more granular, as it would be made with a view to
fit a specific methodology or to
review the appropriateness of specific assumptions and parameters.
3.87 The requirements to set up adequate internal processes and
procedures, in the context of Article
82, should not relate to such a granular level, but it should
consider data quality from an overall
perspective for the purpose of the calculation of technical
provisions, without necessarily relating
it to the application of particular methodologies.
3.88 In the context of a provisioning analysis, it may be
necessary to complement the internal data
available with external data supplied by third parties or market
data.
When assessing the general requirements on data quality –
appropriateness, completeness and accuracy
–this external and market information should be part of the
analysis.
3.89 In the particular case of external and market information,
the verification of the three criteria
implies:
• Appropriateness and completeness: the assessment of these
criteria is performed at the portfolio
level, considering the set of available data necessary to fully
carry out the intended analysis (in
particular, when setting one particular assumption).
Where relevant, the assessment of appropriateness and completeness
shall also be performed at a more
granular level, including if necessary the analysis relating to
the individual items.
Undertakings are expected to verify that the inclusion of the
individual items of external and market
information contribute towards the enhancement of the
appropriateness and completeness criteria having
regard to the intended purpose of the analysis;
• Accuracy: as individual items of external and market information
have not been collected and
compiled by the undertaking itself, the assessment of its accuracy
is likely to be challenging.
The verification of this criterion will have to consider the
reliability of the sources of information
and the consistency and stability of its process of collecting and
publishing information across time.
3.90 Moreover, whenever adequate, measurement of the quality and
credibility of the available data in
the context of provisioning analysis should have regard to
available industry or market data which is
deemed comparable, having regard in particular to the requirements
set in article 76(3).
Any material
deviations should be identified and interpreted, for instance by
referring to the specificities of the
own portfolio being valued.
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