The January 2010 edition of the Solvency ii Association
newsletter
Dear
Members,
I
want to extend my best wishes for a safe, prosperous and blessed
year to all. I do hope that you will transform the difficulties
around into opportunities.
I know what you
did most of the time during December: You were studying the new
CEIOPS papers, like me, and like many Solvency ii professionals
around the world.
Risk
and compliance managers, senior executives and boards of
directors try to understand what is different now -
from the
European Union, where firms try to comply, to Bermuda and third
countries, where countries try to provide evidence that they are
equivalent.
Today
we will discuss one of the most important new papers:
CEIOPS’’ Advice for Level 2 Implementing
Measures on Solvency II: Technical Provisions – Article 86 f -
Standards for Data Quality
This Paper aims
at providing advice with regard to the standards that should be
met with respect to ensuring the
appropriateness, completeness and
accuracy of the data used in the calculation
of technical
provisions, and with
the specific circumstances in which it would be appropriate to use
approximations, as requested in Article 86 (f) of the Solvency II
Level 1 text.
Data used to feed the best estimate
calculation can have an essential impact on its outcome.
Hence it is
necessary to assess the quality of
these data for instance, when necessary, by reconciling them with
those from the
annual accounts or with any other internal statistical database
or, by ensuring consistency with any external data used, showing
the differences and explaining reasons and consequences of any
detected misalignment .
Quality of data
is crucial in the scope of valuation of technical provisions,
mainly, because:
• The more complete and correct the data is,
the more consistent and accurate final estimates will be;
• The application of a wider range of methodologies for
calculating the best estimate is made possible, improving the
chances of application of adequate and robust methods for each
case.
• Validation of methods is more reliable and leads to more
credible conclusions, once a reasonable level of quality of data
is achieved.
• Effective comparisons over time and in relation to market data
are possible, which leads, for instance, to a better knowledge of
the businesses in which the undertaking operates and its
performance.
Throughout this paper,
the term ‘data’ is used to refer
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.
In the context
of this paper, 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.
Whereas this
paper is focused on setting out advice in the context of a
valuation of technical provisions, it is noted that the issue of
data quality is also relevant in other areas of a solvency
assessment, for example for the calculation of the Solvency
Capital Requirement (SCR) using the standard formula or internal
models.
CEIOPS considers
that, to the extent appropriate, a consistent approach to data
quality issues needs to be taken
across Pillar 1, without however disregarding the different
objectives and specificities of each area.
Extracts from Level 1 Text
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.
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.
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.
Advice
Explanatory text
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.
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.
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.
Criteria to assess the quality of
data
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.
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.
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.
The following
paragraphs discuss how each of these three criteria should be
interpreted when assessing the quality of data.
Appropriateness
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).
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
Data is considered to be complete if it
allows for the recognition of all the main homogeneous risk
groups7 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.
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).
In principle,
the more heterogeneous the portfolio is, the
more detailed the data should be.
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
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.
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.
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).
Data deficiencies
Frequently the data available to the undertaking may not be fully
appropriate, accurate and complete.
There are two
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.
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.
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.
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.
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.
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).
However,
any such adjustment and the underlying
assumptions should be carefully justified and documented, and
should not overwrite the raw data.
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.
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.
Application of
the principle of proportionality
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.
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).
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.
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.
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.
Requirements on internal processes
and procedures
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.
Data
quality management - Internal processes
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.
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.
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.
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).
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.
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.
Identification, collection and processing of the data
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.
Role
of external auditor and actuarial function
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.
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.
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).
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.
Issues of data quality in the
context of a provisioning analysis and review
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.
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.
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.
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.
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)).
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.
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.
Circumstances where adjustments to
historical data may be needed in the context of the provisioning
analysis
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.
Issues related
to external data or market benchmarks
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.
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.
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.
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.
If
you believe that the Own Risk and Solvency Assessment (“ORSA”) is
the main problem of the European Union's firms, you must read the
BERMUDA MONETARY AUTHORITY'S DISCUSSION PAPER ON THE OWN RISK AND
SOLVENCY ASSESSMENT PROCESS -
www.bma.bm/uploaded/429-091001_BMA_-_Discussion_Paper_on_the_Own_Risk_and_Solvency_Assessment_Process.pdf
ORSA is defined in Bermuda as:
“The entirety of the processes and procedures employed to
identify, assess, monitor, manage, and report the short and
long-term risks an insurer faces or may face and to determine the
own funds necessary to ensure that the insurer’s overall solvency
needs are met at all times”
Looks familiar?
Dear members,
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Solvency ii related risk and compliance topics. Subject matter
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Contact: Ross Fenwick, Managing
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General
Manager, Compliance LLC
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