Actuarial Best Estimates and Proxy Methods

8/3/2019 Actuarial Best Estimates and Proxy Methods

1/58

January 2008

General Insurance Reserving

Actuarial Best Estimates and Proxy Methods

A commentary from the Actuarial Professions General Insurance Reserving Oversight Committee

(GI ROC) to the report from the Board for Actuarial Standards GI Proxy Methods Experts Group

(published September 2007).


2/58

GI Board: Reserving Oversight Committee

GI Reserving Actuarial Best Estimates and Proxy Methods

January 2008

Comments from the UK Actuarial Professions General Insurance Boards Reserving

Oversight Committee (ROC) on Proxy Methods, and on the Board for Actuarial

Standards GI Proxy Methods Experts Groups report (the BAS Report) to the FSA

on GI Proxy Methods (published September 2007), in the context of proposed

Solvency II requirements regarding reserving.

The BAS Report is appended to this response (page 8 onwards).

ROC Membership

Lis Gibson (Chairman)

Caroline Barlow

Neil Bruce

Steven Fisher

Neil Hilary

Ian Hilder

Richard Winter

Introduction

The GI Board and CEIOPS have asked the Reserving Oversight Committee to provide

their comments on behalf of the Actuarial Profession on the subject of Proxy

Methods, and the BAS Report. There has been some concern expressed that the UK

Actuarial Profession may appear to be endorsing the use of Proxy Methods as

described in the BAS Report and it is in the particular context of that concern that we

make these brief comments.

In formulating our comments, we have recognised that BAS is responsible for

technical actuarial guidance, and we have not sought to provide comments on the

sections of the BAS Report which deal with the technical aspects of Proxy Methods

and benchmark data. Rather, we have reviewed and commented on the principles

behind the potential use of Proxy Methods and the practical and professional issues

which we believe fall within the remit of the Actuarial Profession.

The BAS Report explains its purpose and context as follows:

One of the cornerstones of Pillar I is an Actuarial Best Estimate of technical provisions. Indiscussions between CEIOPS (the Committee of European Insurance and Occupational

Pensions Supervisors) and the Groupe Consultatif (which represents actuarial associations in

the European Union) it was observed that some companies in Europe do not have ActuarialBest Estimates of their technical provisions, either because of lack of knowledge or lack of


3/58

data. It is envisaged that supervisors and actuarial associations will, where appropriate,

encourage insurers to improve expertise and collect any necessary industry-wide data to allow

Actuarial Best Estimates of technical provisions to be produced. In the interim, however, there

is a need to find practical solutions (Proxy Methods), especially for smaller firms, where

Actuarial Best Estimates are not available. Various member states have been asked to form

Expert Groups to consider what Proxy Methods may be possible or appropriate in the absence

of Actuarial Best Estimates. Against this background, the Financial Services Authority(FSA) asked the Board for Actuarial Standards (BAS) to set up an Expert Group to

consider proxy methods for General Insurance (GI) technical provisions from a UK

perspective. This report sets out the thinking of that Expert Group.

Our comments will be in the same context.

Summary of Our Comments and Recommendations

Solvency II requires claims provisions to be (Actuarial) Best Estimates, defined as

being the (discounted) mean of the distribution of possible outcomes.

Proxy Methods are relatively simple methods which can be applied mechanically

without a high degree of judgement, when either insufficient data is available to allow

the use of more sophisticated actuarial methods, or where companies do not have

access to the necessary actuarial expertise.

Proxy Methods should make use of such data as is available, but there may be a need

to supplement them with the use of suitable benchmark data. If insufficient

benchmark data is available, there may be a need to arrange for the collection of such

data to support the use of Proxy Methods.

Where Proxy Methods are utilised, the intention should still be to produce a Best

Estimate of the provisions required. The uncertainty associated with a paucity of data

or the absence of appropriate judgement should be reflected in the risk margin rather

than in the claims provisions themselves. Consideration of risk margins is beyond the

scope of this note.

Best Estimates should be produced using the judgement of a suitably qualified and

experienced person. By virtue of their training and professional standards, suitably

experienced qualified actuaries are ideally suited to exercise this judgement.

A regulatory framework which caters for the setting of reserves in the absence ofappropriate judgement is exposed to failure and potential abuse, as the mechanical

application of methods does not take into account all aspects of the business.

The real issue is not so much proxy methods as proxy judgement.

Appropriate judgement can sometimes be exercised by experienced insurance

professionals who have not qualified as actuaries.

It would be practical and reasonable for a regulatory framework to provide

transitional arrangements or exceptions for smaller companies, allowing suitable non-

actuaries to produce reserves using their experience and judgement.


4/58

We recommend that a European benchmark database be created in time for the

implementation of Solvency II, to make development data, benchmark development

factors and benchmark prior loss ratios available to the market for as many business

groups as possible.

We set out a recommended approach for companies exempt from the actuarial bestestimate basis to follow, which includes the nomination of an alternative expert,

training and the use of central benchmarks as a safety net.

Overview of our Understanding of the BAS Report

The BAS report has been written on the premise that Proxy Methods are methods for

calculating claims provisions which are used by companies who do not use actuaries

to assist in setting their technical provisions.

It is our understanding of the BAS report that Proxy Methods are essentially definedby the authors to be methods used to calculate reserves utilising some or all of

Various historical data Planning and pricing information Industry benchmarks Judgement of insurance professionals, such as claims managers and

underwriters

as a substitute for actuarial judgement.

The BAS Report sets out some examples of methods which might be used by

actuaries as part of their overall methodology, subject to the use of actuarial

judgement, to produce best estimates, or could alternatively be used by non-actuaries

as a proxy for actuarial judgement.

The BAS Report sets out some advantages and disadvantages of Proxy Methods as

used by non-actuaries as a proxy for actuarial judgement. They explain that on some

occasions the judgement of other professionals may be adequate. They also explain

that the use of these methods by actuaries applying judgement is fine.

The BAS Report makes an assumption that when Proxy Methods are used, a degree ofprudence should be built in. They believe that this is the intention of CEIOPS,

although they do also acknowledge that this is inconsistent with the Solvency II

requirement for Best Estimates. They do not explain how such caution is to be

introduced or measured, or how this may subsequently be allowed for in the

calculation of risk margins.

The BAS Report appears to make no reference to the discounting of technical

provisions. However, the Solvency II requirement for a discounted Best Estimate

means that further consideration may need to be given to this aspect.


5/58

The BAS Report also sets out some disadvantages of the use of Proxy Methods,

including the dangers of nave interpretations of past data or benchmarks and the

potential need to allow for future trends beyond the data. They conclude

In short, methods based on the application of benchmark ratios / factors, be they market

derived or from other sources, may produce very unstable results. Such methods are best usedto validate more sophisticated approaches and to inform judgements, rather than being used in

isolation as a basis for setting provisions.

The BAS Report concludes that Proxy Methods cannot be relied upon to provide a

meaningful estimate of future claim costs without appropriate judgement and

understanding, and that such methods are best used to validate more sophisticated

approaches and to inform judgements, rather than being used in isolation as a basis for

setting provisions. They also appear to conclude that reliance on the judgement of

other insurance professionals may sometimes be a valid proxy for reliance on the

judgement of actuaries.

Our Thoughts on Proxy Methods

The request for consideration of Proxy Methods was prompted by the observation that

some companies in Europe do not have Actuarial Best Estimates of their technical

provisions, either because of lack of knowledge or lack of data. Solvency II requires

claims provisions to be (Actuarial) Best Estimates, defined as being the (discounted)

mean of the distribution of possible outcomes. We note that a previous paper prepared

on behalf of the Actuarial Profession sets out the considerations to be taken into

account in setting such best estimates.

Essentially the calculation of Best Estimate claims provisions depends on all of:

The availability of suitable credible data The application of appropriate calculation methods The application of judgement based on experience and knowledge of the business

and market conditions.

Our interpretation is that Proxy Methods may be required when either insufficient

data is available to allow more sophisticated actuarial methods to be applied, or where

companies do not have access to the necessary actuarial expertise, knowledge or

experience required to apply more complex methods or to exercise judgement as to

the appropriateness of different methods or the validity of the answers produced. ThusProxy Methods are likely to be relatively simple methods which can be applied

mechanically without a high degree of judgement.

It is likely that for smaller companies and for companies which have not previous

applied actuarial methods, there will be issues regarding the availability of sufficient

data. For smaller companies (or indeed for small lines of business within larger

companies), the volume of business may mean that the data available is sparse, or that

the experience is volatile. For companies without previous actuarial involvement,

there may be a lack of suitable historical data to use as the basis for projections, and

the nature of the companys systems may make it difficult to reconstruct such

historical data. In these circumstances, there will be a need for Proxy Methods, whichshould make use of such data as is available but may need to supplement it with the


6/58

use of suitable benchmark data. If insufficient benchmark data is available, there may

be a need to arrange for the collection of such data to support the use of Proxy

Methods.

We believe that where Proxy Methods are utilised, the intention should still be to

produce a Best Estimate of the provisions required. There will of course be a need toconsider the credibility of the data available, and to supplement this with suitable

benchmark data where necessary and feasible. We acknowledge that lack of

appropriate data will increase the uncertainty associated with the projections, and

there may be a tendency to take a more cautious view to compensate for this.

However, unlike BAS, we believe that this uncertainty should be reflected in the risk

margin rather than in the claims provisions themselves. (We also note that there is a

need to consider how Proxy Methods for the calculation of risk margins can be

developed, but that is outside the scope of this note.)

We believe that ideally Best Estimates should be produced using the judgement of a

suitably qualified and experienced person. Article 47 of the EC Draft Directive(quoted in section 5.1 of the BAS report) clearly envisages the existence of an

actuarial function to provide this expertise. By virtue of their training and professional

standards, we believe that suitably experienced qualified actuaries are ideally suited to

exercise this judgement.

Whatever methods, models or benchmarks are applied, the absence of experienced

judgement and interpretation may render the results potentially misleading. A

regulatory framework which caters for the setting of reserves in the absence of

appropriate judgement is therefore exposed to failure and potential abuse.

Most, if not all, reserving methods can however be used by anyone, with or without

appropriate experience or judgement. Sometimes using judgement-free methods can

provide useful objective benchmarks and can assist in illustrating the context in which

judgement is being exercised, and the scale of uncertainty. However, regulatory

reserves should not be based on judgement-free methods. We believe that the real

issue is not so much proxy methods as proxy judgement.

We believe that appropriate judgement can sometimes be exercised by experienced

insurance professionals who have not qualified as actuaries. We believe that it would

be practical and reasonable for a regulatory framework to provide transitional

arrangements or scale-based exceptions allowing suitable non-actuaries to producereserves using their experience and judgement.

Our Recommendations to CEIOPS

In order for proxy methods to be a viable alternative to the full standard actuarial

approach for calculating best estimates, we make the following recommendations:

We recommend that a European benchmark database be created in time for the

implementation of Solvency II. The benchmarks should include the aggregated

premium, paid claim and incurred claim development data from the relevant classesof business. The data required to populate such a database would of course have to be


7/58

collected in such a way as to comply with European Competition Law. The most

natural way for this would be via the statutory returns which would need to be

designed accordingly.

We recommend that on an annual basis aggregate market premium, paid claims and

incurred claims development patterns, and average market loss ratios be calculatedusing full data and appropriate actuarial judgement and made available to the market

as benchmarks. This could be financed by charging insurers and advisors a small fee

for annual access, perhaps with insurers of below a minimum size being granted free

access.

We recommend that any insurance companies below a particular size, to be decided,

very small companies, be granted perpetual exemption from the need to use a

qualified actuary to produce actuarial best estimate reserves, provided they continue

to qualify in terms of their size. We recommend that they have an alternative

approach as set out below.

We recommend that companies above the very small threshold but below a second

threshold, small companies be granted a transitional period during which they can

follow the alternative to an actuarial best estimate reserving basis, but after which

they must revert to the actuarial best estimate basis.

We recommend that transitional arrangements need to be set in place to collect

appropriate data.

The Recommended Alternative Approach

We recommend that very small and small companies who opt for the alternative to

actuarial best estimate reserving, must nominate their proxy reserving expert, and

state that persons relevant skills and experience. That person must attend a course

(say of 2 or 3 days, to be run centrally) and must provide evidence that they are

suitably experienced to provide a proxy to actuarial judgement in the context of their

company.

The nominated expert will be required to perform and report on certain calculations

such as chain ladder factors to ultimate, ultimate loss ratios and average claim sizes,

using the centrally produced industry benchmarks described above, on the classes

relevant to their company. This should not be positioned as directing or evenencouraging the adoption of these benchmark results as their reserve estimates. The

nominated expert must explain their own reserving figures in the context of the results

using the industry benchmarks, setting out the similarities and differences and reasons

for them.

We believe that this is a practical way to provide a minimum standard, which should

not be unduly onerous for small and very small companies, especially mono-lines.


8/58

UK Expert Group

Report On

General Insurance

Proxy Methods

Prepared by GI Proxy Methods Expert Group

Version 5 Published September 2007


9/58

2

INTRODUCTION

Solvency II is an EU project which (quoting from the ECs Framework for Consultation) aims todevelop a new solvency system to be applied to life assurance, non-life insurance and reinsuranceundertakings, which Member States and supervised institutions are able to apply in a robust,

consistent and harmonised way.

Solvency II will be built on three pillars (similar to Basel II):

Pillar I: quantification of capital requirementsPillar II: supervisory review processPillar III: market analysis of published data

One of the cornerstones of Pillar I is an Actuarial Best Estimate of technical provisions. Indiscussions between CEIOPS (the Committee of European Insurance and Occupational PensionsSupervisors) and the Groupe Consultatif (which represents actuarial associations in the EuropeanUnion) it was observed that some companies in Europe do not have Actuarial Best Estimates oftheir technical provisions, either because of lack of knowledge or lack of data. It is envisaged thatsupervisors and actuarial associations will, where appropriate, encourage insurers to improveexpertise and collect any necessary industry-wide data to allow Actuarial Best Estimates oftechnical provisions to be produced. In the interim, however, there is a need to find practicalsolutions (Proxy Methods), especially for smaller firms, where Actuarial Best Estimates are notavailable. Various member states have been asked to form Expert Groups to consider what ProxyMethods may be possible or appropriate in the absence of Actuarial Best Estimates.

Against this background, the Financial Services Authority (FSA) asked the Board for ActuarialStandards (BAS) to set up an Expert Group to consider proxy methods for General Insurance(GI) technical provisions from a UK perspective. This report sets out the thinking of that ExpertGroup.

GI Proxy Methods Expert Group 12 September 2007


10/58

3

SUMMARY

Extent of the use of Proxy Methods in the UK

We dont believe there is any significant use of Proxy Methods in the UK. The vast majority of UKinsurance companies use actuaries in some capacity to help set their GI technical provisions.

Types of Proxy Method

In the rare instances where Proxy Methods have been used, they include:

A. Applying expected loss-ratios (typically from Plan or Pricing / Underwriting assumptions)B. Judgement of experienced underwriters / claims staffC. Applying benchmark loss-ratios (from external data)D. Using benchmark claims development factors (from external data)E. Using benchmark survival ratios (typically for run-off / latent claims exposures)F. Case estimates alone (where known to be conservative, or for Catastrophe claims, say)G. Using IBNR ratios (as a percentage of case estimates or premium, for example)

H. Application of statistical methods by non-actuaries

Details of these Proxy Methods are given in section 3.

Availability of benchmark information

The main likely source of benchmark information in the UK (outside Lloyds) is the annual FSA returns.These include loss-ratio and claim development information that can be used, with caution, to provideindicative loss-ratio or claims development factors.

Lloyds of London have developed an in-house reserve benchmarking pack which syndicates can useto compare their reserves to market information in various ways.

The Association of British Insurers publishes some high level market information on claims andprofitability trends but this is not suitable to provide benchmarks for setting provisions.

Advantages and Disadvantages of Proxy Methods

In some situations Proxy Methods can be perfectly valid and may well be the sort of method that anactuary would use if they were involved in setting provisions. This is typically the case for classes ofbusiness which have no material, or credible, claims experience to act as a base (for example cyberrisks or political risks), or classes of business that are being written for the first time by an insurer (forwhich, initially, market benchmark or Plan / expected loss-ratios may be the only guide). Settingprovisions using the judgement of underwriting / claims staff for small classes of business, where thoseinvolved have a detailed knowledge and understanding of the business (and the cost of significantactuarial involvement may be disproportionate), may also be a perfectly reasonable approach.

There are, however, significant risks in using market benchmark data or the nave application ofstandard claims development factors. The past is not necessarily a guide to the future! There are awhole host of reasons why benchmark ratios and factors may be inappropriate. These include changesto: the external legal environment; claims handling processes; features of the business such asexcesses or deductibles; the mix or type of business being written; randomness of past claimsexperience. In short, methods based on the application of benchmark ratios / factors, be they market-derived or from other sources, may produce very unstable results. Such methods are best used tovalidate more sophisticated approaches and to inform judgements, rather than being used in isolationas a basis for setting provisions.


11/58

4

TABLE OF CONTENTS

SECTION PAGE NUMBER

Introduction 2Summary 3

1. What weve done 52. Decision-tree for Proxy Methods 63. Types of Proxy Method 84. Benchmarks 125. Advantages and Disadvantages of Proxy Methods 15

Appendix I Questions we asked consultancies about Proxy MethodsAppendix II Sample benchmark information from Lloyds of LondonAppendix III Bibliography


12/58

5

1. WHAT WEVE DONE

1.1 Who we are .

The UK Expert Group comprises:

Henry Johnson (and Jerome Kirk) / Lloyds of LondonJames Upson / Financial Services AuthorityJulian Lowe / Aviva (and member of the Board for Actuarial Standards) (Chair)Kathryn Morgan / Co-operative Financial Services (when the Group formed) (Deputy-Chair)Louise Pryor / Board for Actuarial StandardsRachel Griffiths / Association of Mutual Insurers (AMI)Yvonne Braun / Association of British Insurers (ABI)

1.2 Who weve consulted with .

The Expert Group has considerable experience of setting GI provisions in the UK (and overseas).However we thought wed supplement our experience by approaching a number of consultancies,namely Deloittes, PwC, KPMG, E&Y and Mazars.

We were aware that theres potentially an element of self-selection if we speak to actuaries atconsultancies. By definition, actuaries would only tend to deal with the sorts of insurance companieswho deal with actuaries! However, the various consultancies do have experience of companies whodont routinely use actuaries to any great degree, so were able to give perspectives as to what methodsand techniques they had seen in such companies. Although Mazars has an actuarial function, they alsohave a number of clients who dont use this (or other actuarial support), so were able to give someuseful insight too.

Finally, through the AMI, we spoke to Chief Executives of a number of smaller insurance companies,whose size doesnt warrant an in-house actuarial function.

Our consultations revealed that nearly all UK insurance companies have some actuarial involvement inthe setting of their technical provisions.

1.3 What benchmark information weve looked at .

The FSA, ABI and Lloyds of London are all represented on our Expert Group. The relevantrepresentatives provided information about the sort of benchmark information that is collected undertheir auspices, which is described in section 4.


13/58

6

2. DECISION-TREE FOR PROXY METHODS

2.1 When is a Proxy Method not a Proxy Method?

As described in the Introduction, the cornerstone of Pillar I of Solvency II is an Actuarial Best Estimateof a companys technical liabilities. The potential need for Proxy Methods arose because of concerns

that some companies do not have their technical provisions set with actuarial involvement. Hence wehave taken this to mean that Proxy Methods only need to be considered for companies that do not useactuaries to help set their technical provisions. So, for example, some of the Proxy Methods wedescribe in section 3 may, in fact, be used by actuaries. For our purposes they are deemed to be ProxyMethods if they are applied by non-actuaries.

Its also important to realise that whilst actuaries may be involved in setting provisions in mostcompanies in the UK, the ultimate responsibility for setting provisions sits with the management of theinsurer (based on actuarial advice as to what the underlying Best Estimate of the liabilities might be).

2.2 What is the decision-tree for using different types of Proxy Method?

As noted above, our starting point is that any method used by an actuary constitutes an Actuarial BestEstimate. So in what follows we are only referring to instances where methods are applied by non-actuaries.

Decision One: is this an entirely new class of business for both your company and the industry?

Entirely new types of insurance wont have any historic data to act as a guide, nor will there be anyindustry benchmarks. If the answer to this question is yes, then the only source of guidance as to thelevel of reserves will be the expected or Plan loss-ratios, Proxy Method A in section 3. Theseexpected or Plan loss-ratios should, one would hope, be based on market research and perhaps someunderlying research and assumptions about the exposure to the risk of loss.

An example of a relatively new type of insurance is cyber risk cover. This provides insurance forcomputer attacks by insiders or hackers that may include destruction or alteration of data, inability toaccess services, or downstream liability for attacks against other computers or networks from onesown systems. Such risks simply did not exist a couple of decades ago.

If the answer to this question is no, then one can proceed to Decision Two.

Decision Two: is this a new class of business for your company (but not the industry)?

Companies may start writing new classes of business (for example Motor products having onlypreviously written Household insurance). Whilst such classes of business may have been in existencein the market for many years, a company writing such business for the first time will not have any claimsor profitability experience to act as a guide. In such instances Proxy Methods A, B or C may beappropriate. These either use the expected or Plan assumptions when the business was written, asper Decision One above, or supplement this with the judgement of staff (who may have experience ofthe business from other companies) or the application of industry benchmarks for typical levels ofprofitability (hence claims costs).

Once such a new class has been written for a while, other Proxy Methods may be possible, once onehas some credible historic claims data to act as a base. Classes may also be new but similar toexisting products, in which case one might also have credible, relevant, data to allow other ProxyMethods to be used.

If the answer to this question is also no, then one can proceed to Decision Three.


14/58

7

Decision Three: is there any credible historic claims data for your own company ?

If the answer to this question is yes, then the class of business is not new to either your own companyor the industry (from Decisions One and Two) and you have a credible amount of historic claims data. Inthis case there is enough information to perform a number of Proxy Methods. The initial potential ProxyMethods A, B and C may all still be used. In addition Proxy Methods D-H may also be used.

Method D uses benchmark development patterns to apply to ones own claims data. For exampleassuming that 75% of claims have been paid after one year, a sensible allowance for future claims maybe to take one third of the claims paid to date (that is, the missing 25%) as a provision.

Method E is only intended to be used by companies in run-off, which typically may have exposure toasbestos-related or other types of latent claims. A typical Proxy Method in these circumstances may bethe application of some standard, industry, survival ratios that estimate the future liability as a multipleof the rate of paying, or reporting, of claims.

Methods F and G are similar to Method D in that they are implicitly making assumptions about likelyfuture levels of claims given certain information about the claims paid or reported to date. For exampleMethod F (just using case estimates) assumes that at the point the provisions are set that all claims for

a period have been reported, no previously settled claims will be reopened and case estimates forreported claims will be more than adequate to pay for the final cost of claims (or that any surplus in thecase estimates more than outweighs any future reporting, or reopening, of claims).

Method H comprises doing the sort of projections and assessments that actuaries would do, but thatthis is being performed by non-actuaries.

If the answer to Decision Three is no, then you are probably dealing with a type of business for whichthe claims experience is very sparse. For example a Catastrophe reinsurance contract with a high levelof excess may be designed and priced such that it only expects to incur any claims once every twentyfive years. It would therefore be reasonable to write such business for several decades and not receiveany claims. Other more esoteric types of insurance may insure very rare events the possibility of Elvisbeing seen in your local fish and chip shop, for example. For such classes there may be a very small

possibility of ever receiving any claims. In these circumstances one comes back full circle to ProxyMethods A, and possibly B, as the only reasonable approaches to arriving at expected claims costs.


15/58

8

3. TYPES OF PROXY METHOD

3.1 What types of Proxy Method are there?

From the Expert Groups own knowledge and the further input from a number of consultancies andsmaller companies, we have categorised Proxy Methods into a number of types, Methods A-H below.

Methods A and B can be described as Expectation and Judgement. Methods C-E can be described asIndustry benchmarks. Methods F and G might be called Simple factor-based methods. Method H isStatistical techniques applied by non-actuaries.

As previously noted, we are assuming all these Proxy Methods are applied by non-actuaries. Becausethere is considerable scope for the Proxy Methods to be unstable and unreliable, a further assumptionwe have made is that when insurers use Proxy Methods they should err on the side of caution. This isat odds with the intention of Solvency II (that an Actuarial Best Estimate should form the basis of Pillar Iand be just that a Best Estimate with no deliberate caution or margin). The additional prudencereflects the lack of actuarial involvement and our understanding of the intention of CEIOPS regardingProxy Methods.

It is important to have some way of tracking how the claims experience develops if Proxy Methods areused. For example being able to split out the claims experience for prior periods to see whether theunderlying assumptions made in setting provisions have been systematically too high, or too low, toassess the suitability of the Proxy Method. Where possible it would also be good practice to consider anumber of Proxy Methods

3.2 Expectation and Judgement methods

Method A: Applying expected loss-ratios

When a company writes a class of business it will have made some initial, or Plan, assumptions aboutthe likely level of claims experience. For classes of business which the company has been writing for awhile, future loss-ratios may be based on past experience trended forwards to allow for claims inflation,rating action and changes in the mix or characteristics of the business.

For entirely new classes of business (to the company and the industry), or classes of business with verysparse data, the company should have performed some underlying research into the likely exposure toclaims. For example for high layers of Catastrophe reinsurance cover there is a considerable body ofmeteorological data on expected frequency and severity of windstorms and a number of companieshappy to sell you their in-house models of windstorm exposures. For relatively new risks like cyberinsurance, companies should have researched the underlying dynamics of the types of risk beinginsured and looked at whatever market research there is about the types of risk involved, or perhapsperformed their own surveys or analysis.

In the absence of past insurance experience or any credible claims data, using the assumption implicitin the pricing basis when the business was written may be the only sensible approach to settingprovisions. Depending on the class of business, the provision may be set at this initial Plan, orexpected, level and then reduced over time as any actual claims experience develops. For example onecould draw down the initial provision by the amount of any payments made. Or if you thought all claimswere likely to be reported over a period of N years, reduce the provision down to zero by 1/Nth per yearover that N year period.


16/58

9

Method B: Judgement of experienced underwriters / claims staff

The underwriters / claims staff may have worked in the industry for many decades and have anexcellent knowledge of the companies own classes of business and market intelligence about claims orproduct developments. The application of such detailed knowledge and experience may be simply gutfeel, or it may be combined with a more scientific, but often ad hoc, review of loss development

triangles or other claims statistics. In smaller companies, or perhaps those with unusual niche products,this may be an entirely justifiable approach, particularly when the scale of the business would notwarrant more detailed actuarial involvement (and the lack of actuarial involvement is compensated forby an element of prudence).

3.3 Industry benchmark methods

Method C: Applying benchmark loss-ratios

As well as a companys own Plan or expected loss-ratios, companies may also look to marketinformation on loss-ratios as a guide. Such market information may be available through publishedresults, friendly reinsurers (who might provide Quota Share reinsurance in cases where a company isexpanding into a new market), or statutory returns (see section 4.2). Some companies may be trying to

follow the prices of other companies, or use other companies rates as a guide, so may have Proxymarket profitability information available by that route.

The same considerations would then apply as Proxy Method A. The assumed market loss-ratio may beused as a guide to setting the companys provisions, noting the need for caution and prudence referredto earlier.

Method D: Using benchmark claims development factors

The application of this sort of benchmark information requires a little more sophistication. In the UK theFSA requires companies to submit FSA returns which show a considerable amount of informationabout the loss development of each company see section 4.2. This includes the amount and numberof claims paid and reported for a series of years after the initial Accident (or Underwriting) year in which

the business is written.

Someone with a modicum of statistical ability and knowledge of general insurance could use the FSAreturns to arrive at some default claims development assumptions, either based on a number of largercompanies or for the industry as a whole. There are a number of companies who provide software to letyou access FSA returns for the entire UK market, or some companies publish research analysing theFSA returns (investment analysts and consultancies). It could be that a company has its own claimsdevelopment data, but the company is so small that, for a particular class, the developments may beeasily distorted by large, or exceptional, claims experience. In such cases the typical development oflarger, more credible, data may be used as a guide. Auditors or consultants may also be able toprovide benchmark claims development factors.

The essence of any claims development method is to use the past development of claims costs as a

guide to the future. So, for example (as described in section 2.2), one may observe that in past periodson average 75% of claims have been paid after one year. This might mean that a sensible allowance forfuture claims may be to take one third of the claims paid to date (that is, the missing 25%) as aprovision. There are, of course, many reasons why such an approach may be flawed, which is why theSolvency II model is to use actuaries to produce Best Estimates, taking account of the factors that maydistort such methods see sections 3.5 and 5.2 for some more references to these distortions.

It is good practice to produce estimates of future claims costs using a range of techniques. So, forexample, one could use benchmark data to perform projections based on claims payment data, claimsincurred data (payments plus case estimates) and project numbers of claims multiplied by averagecosts.


17/58

10

Method E: Using benchmark survival ratios

As noted in section 2.2, this Proxy Method is only intended to be used by companies in run-off (orongoing companies who have some business in run-off), which typically may have exposure toasbestos-related or other types of latent claims. A typical Proxy Method in these circumstances may bethe application of some standard, industry, survival ratios that estimate the future liability as a multiple

of the rate of paying, or reporting, of claims.

For example if a company has exposure to UK asbestos claims, one could use the benchmark survivalratios from the UK Actuarial Professions GIRO paper: UK asbestos the definitive guide

(1)as a

sensible starting point to estimate future liabilities. This would involve taking the assumed underlyingrate of reporting of, say, mesothelioma claims and multiplying this by the relevant survival ratio (in thiscase 68) to produce an undiscounted estimate of future claims costs.

Other sources of benchmark survival ratios may be publications that refer to industry survival ratios (forexample a rating agencys review of asbestos and latent claims exposures), or helpful information fromones own firm of auditors, or consultants, as to typically assumed survival ratios.

3.4 Simple factor-based methods

Method F: Just booking case estimates alone

If companies routinely set case estimates that are too high, simply booking case estimates may be aprudent Proxy Method (noting our prior assumption that CEIOPS intends Proxy Methods to err on theside of caution). Of course initially there may be delays in reporting of claims so this runs the risk thatthe provision for the current years business is too low. However if a business is in a fairly steady state,with case estimates for prior years comfortably too high, then the likely surplus for prior periods maymore than outweigh any potential deficit due to late reporting of claims for the current year.

It would be important for a company who uses this approach to monitor the development of claims forindividual cohorts of business, to ensure that the assumption that case estimates are typically too high

continues to hold good. Companies would have to be alert to any changes in case estimatingphilosophy, or changes in the nature of the business or the external claims environment, that mayinvalidate this assumption (see sections 3.5 or 5.2 for more details). Whilst the assumption that likelysurpluses for past periods may more than outweigh the omission of late reported claims from currentperiods, should a company start to grow significantly, this assumption may also not hold good.Generally we do not regard this as a satisfactory Proxy Method.

Method G: Using IBNR ratios

This Proxy Method is a variation on Proxy Methods D and F. Some companies may have developedapproximate assumptions over time that a suitable IBNR allowance might be a certain percentage ofcase reserves, or a certain percentage of premium. In some cases overseas there may be crude steersfrom local regulators that IBNR reserves should be at least X% of premium.

The same concerns for Proxy Method F apply, with there being many reasons why such methods maynot be reliable over time. Like Proxy Method F it would be important for companies to have ways oftracking this assumption, to see if it holds good, or spot sooner rather than later if the amount of IBNRrequired seems to be higher, or lower, each year.


18/58

11

3.5 Statistical techniques applied by non-actuaries

Method H: statistical techniques applied by non-actuaries

In some companies the analysis used to set provisions is performed by very experienced non-actuaries,who have a considerable amount of skill and expertise and who carry out their work in a thoroughly

diligent and professional fashion. At the other extreme, it is possible for enthusiastic amateurs to pick upa text book and apply some simple chain-ladder techniques without any real appreciation of thedangers in doing so or the types of checks and balances that should accompany a high standardexercise to set provisions.

It is not our intention to write a text book on how to perform actuarial techniques there are manypublications which describe the steps involved and the potential pitfalls for the unwary. The UK ActuarialProfession has set out some considerations that should apply when estimating general insuranceprovisions in their paper: Best Estimate provisions for General Insurance

(2). The UK Actuarial

Profession also recently sponsored some work to look at reserving practices in the UK which makes anumber of recommendations as to good practices to employ when performing a reserving exercise: Achange agenda for reserving. Report of the General Insurance Reserving Issues Taskforce (GRIT)

(3).

Any non-actuaries involved in setting provisions would be well advised to read these and similar

publications to gain an appreciation of the sorts of consideration that should be borne in mind. Section 5describes some of the issues to look out for with any method of estimating future claims costs thatwould apply particularly to any of the Proxy Methods described above.


19/58

12

4. BENCHMARKS

4.1 Benchmarking data in the UK

The Expert Group reviewed the types of benchmark information available in the UK. The main likelysources are the FSA returns, ABI data and the reserving benchmarking exercise performed by Lloyds

of London. Each of these is described in more detail in the following sections.

There may be other, softer, sources of benchmarking data, for example competitors report andaccounts, industry publications looking at market loss-ratios or claims developments and personalknowledge of ones own staff. The various types of hard and soft benchmarking information may beused to help produce Proxy Methods B-D described in section 3.

In the past insurers may have informally exchanged information to provide each other with technicalassistance. Competition Law means it is now far harder to do so and insurers need to ensure they arenot engaging in any anti-competitive behaviour when they share information.

4.2 FSA returns and its use for benchmarking

For those not familiar with the UK insurance market, all insurers carrying on business in the UK (other

than EEA firms subject to the insurance or reinsurance directives carrying on business through freedom

of establishment or provision of services, other EEA firms availing themselves of treaty rights and non-

directive friendly societies) must provide annual accounts and supplementary information in a

standardised format to the UK regulator, the Financial Services Authority (FSA), in a document called

the FSA return. The FSA returns are available to the public. Where it is unduly burdensome to provide

the full detail in the returns (especially for smaller firms), the FSA may exempt the firm from providing

some of the detailed information required.

There are different types of return; a "Global return" reports the entire world-wide business of the firm,

and (for most third country firms) branch return reports only in respect of the branch. The requirement

as to whether a Global return, UK branch return or EEA branch return (or some combination of them) is

provided depends on the location of the head office and the type of firm.

There are a number of companies (such as Synthesys) which collate FSA returns data and sell

software databases containing data from the returns of most or all insurers in the UK market.

The FSA uses the returns as a key source of information to monitor the financial resources of an insurer

and, for GI business, to assess retrospectively the adequacy of the insurer's claims provisions, both by

reviewing the claims development of a firm over some years and by comparing the level of claims

provisions between insurers. However, these analyses tend to be a starting point for discussion with the

firms, recognising that there may be sound explanations for an apparent low level of claims provisions.

Data in the returns is normally split into the following classes of business (no detailed information is

provided separately for small classes of business):

Accident & Health

Motor (split between Personal Motor and Commercial Motor)

Aviation

Marine

Goods in Transit

Property (split between Household & Domestic and Commercial Property)

Commercial Liability

Financial Loss (split between Personal Financial Loss and Commercial Financial Loss)

Miscellaneous Direct and Facultative

Balance Direct and Facultative (lumping together any small classes)

Treaty Reinsurance (split between Non-Proportional Treaty, Proportional Treaty and Miscellaneous)

Balance treaty reinsurance (lumping together any small classes of treaty reinsurance)


20/58

13

One form which could be highlighted for its usefulness in benchmarking is Form 23 of the returns. In

this Form, firms provide claims data split by class and accident year. For example, the ratio of Incurred

But Not Reported claims (in column 6) to the corresponding reported claims (in column 5) could be

used as a benchmark. Likewise, the claims ratio (in column 13) is of interest. It is also possible to use

the development of claims from the beginning to the end of the year as benchmark development factors

for a chain ladder exercise to help set provisions. However, any benchmarking against other firms

contains a number of dangers, as mentioned in section 5.2. In addition, if you benchmark against only afew (or even one other) firms, it is possible that your benchmark is being distorted by special factors,

such as a transfer of business, reinsurance arrangements or exceptional claims.

The FSA has no plans at present to publish benchmark information to assist in setting claimsprovisions.

4.3 ABI data

The ABI collates various statistics from its members. It publishes some of this aggregate data and more

detailed information is available on the Members Only section of its website. The information covers a

range of classes of business. By way of illustration for Motor it includes:

Motor statistics

This section contains statistics on premiums and claims, commission and expenses, change in

provisions, equalisation reserves, underwriting result, operating ratios, and overseas data, for the UK

and worldwide.

Annual Motor Statistics

This shows claims split by payment type with particular attention paid to bodily injury claims.

Quarterly Motor Claims

Market trends in the number of all claims and, in particular, the number and cost of all theft claims.

Analysis Of UK Motor Market

These reports look at the experience of the private and commercial motor markets over an eleven year

period by analysing data extracted from companies statutory returns to the FSA.

The level of information is such that its unlikely to be of any assistance for any benchmark ProxyMethods.

4.4 Lloyds of London reserve benchmarking exercise

In the UK the words reserve / reserving are often used interchangeably with provision / provisioning.Whilst in the rest of the report we have generally referred to provisions, in this section we have followedLloyds own nomenclature and referred to reserves / reserving.

Lloyds undertakes an annual relative reserve benchmarking exercise. The main purpose of theexercise is to highlight and gain an understanding of the syndicates reserve levels within the market.The exercise can also be used to identify potential anomalous reserving positions that may need furtherinvestigation.

For each syndicate a unique benchmark is constructed and compared to. The benchmark syndicate iscalculated from the market data (excluding the syndicate in question) adjusted to reflect the underlyingmix of business of the syndicate. The rebasing is required to ensure suitable correspondence betweenthe syndicate and its benchmark and is conducted at a year of account/class of business level. Lloyds

works with around 50 low level classes of business.


21/58

14

A number of Key Performance Indicators (KPIs) are calculated for both the syndicate and thebenchmark over various time periods and brought together during the analysis. Reserve benchmarkingnecessarily includes large volumes of output as considering single or simple reserving measures canoften be misleading. This in turn can lead to results becoming complex and difficult to interpret. Toovercome this, Lloyds have extended the exercise to include the indexing of results which allowsagents to be easily ranked for comparative purposes.

The key points to consider when constructing the benchmark analysis are:

The analysis must be conducted at a low enough level to allow for differences in behaviourbetween classes of business.

The analysis focuses on relative reserve strength. It does not consider reserve adequacy.

Numerous measures are required, preferably over time, to obtain a sound understanding ofan underlying entitys reserves.

The exercise focuses on four key areas both gross and net of reinsurance:

Reserve strength

The absolute levels of reserves are considered. The driving principle is that the larger the reserves held,relative to payments and ultimates, then the stronger the reserves are. The four measures applied toestimate reserve strength are: reserves as a percentage of ultimate claims, IBNR as a percentage ofultimate claims, case reserves as a percentage of incurred claims and survival ratios.

IBNR utilisation

The proportion of the IBNR held at the start of a calendar year that is used (incurred) during the yearshows how quickly the IBNR is being burnt. The slower the IBNR burn the stronger the reserves arejudged to be. The three measures for IBNR utilisation are IBNR burn over each of the last 3 calendaryears (2006, 2005 and 2004 for the current exercise).

Reserving over time

It is important to understand whether agents tend to increase or decrease their estimate of ultimateclaims over time. Those that consistently increase will have tended to set weaker reserves initially andvice versa. The two measures for reserving over time are to compare the ultimate claims projected as atnow (year-end 2006) for a given year of account to the ultimate claims set at the end of year 1 and theend of year 3.

Quality of business

Quality of business is important when considering reserve strength since better business will generallyrequire lower nominal reserves. As the measures above look at nominal reserving, there needs to besome allowance for better quality business. If all the other measures considered are equal, betterunderlying business would suggest stronger reserves. The measure used for quality of business is paidloss ratio. Ultimate loss ratios are not used because these are dependent on reserve strength whichmay distort results.

These four measures are ranked and combined to form a single overall Reserve Benchmarking Index(RBI).Separate RBIs are provided gross and net of reinsurance. The overall RBIs can then be used todirectly compare the relative reserving of syndicates within the market. The results are compiled and fedback to the syndicates within the market. The analyses are presented at varying levels of granularityfrom single measures for a syndicate as a whole down to information on individual years of accountover time. It would be possible to further break results down to a class of business level but this wouldinclude voluminous output that would become less useful.

A copy of an example syndicate pack is included at Appendix II. This is based on a hypotheticalsyndicates results and is for illustration only.


22/58

15

5. ADVANTAGES AND DISADVANTAGES OF PROXY METHODS

5.1 Why would you use Proxy Methods ?

As noted in section 2, we have assumed that Proxy Methods only need to be considered for insurersthat do not use actuaries to help set their technical provisions. The clear steer from the development of

Solvency II however is that an Actuarial Best Estimate should be at the heart of Pillar I. The latest ECDraft Directive, published on 10 July 2007, states (in Article 47) that:

1. Insurance and reinsurance undertakings shall provide for an effective actuarial function to undertakethe following:

(a) to coordinate the calculation of technical provisions;(b) to ensure the appropriateness of the methodologies and underlying models used as well as theassumptions made in the calculation of technical provisions;

(c) to assess the sufficiency and quality of the data used in the calculation of technical provisions;

(d) to compare best estimates against experience;

(e) to inform the administrative or management body of the reliability and adequacy of the calculation of

technical provisions;

(f) to oversee the calculation of technical provisions in the cases set out in Article 80;(g) [The List goes on .] .

Whilst all the above is in Draft, clearly it is expected that actuaries should be heavily involved in thecalculation and oversight of GI technical provisions.

However for small companies, or those writing niche products, the cost of significant actuarialinvolvement may be disproportionate. In such cases, as noted in section 3, we understand (and haveassumed that) it is the intention of CEIOPS that Proxy Methods should err on the side of caution. Itwould be up to the management of the company, and the regulator, to form a view as to the capability ofthose involved in setting the provisions. Quoting from Article 47 again, in section 2 this describes thecapabilities of those performing the actuarial role which include:

sufficient knowledge of actuarial and financial mathematics and able where appropriate todemonstrate their relevant experience and expertise with applicable professional and other standards.

This doesnt preclude non-actuaries being involved but notes the experience/expertise required and theneed to have some sort of standards. As noted in section 3.2 under Proxy Method B, using the judgement of non-actuaries (typically underwriting / claims staff) to set provisions, may be entirelyappropriate, subject to the requirements for experience / expertise and standards noted above.

5.2 What are the dangers in using Proxy Methods ?

Many of these have been referred to already. The reason that the Draft EC Directive places such

responsibility on actuaries is that mechanical or simplistic methods of setting provisions, applied withoutthe necessary experience or understanding of the nature of the business, can produce very unreliableresults.

The starting premiss of any form of projection is that the past is, in some sense, a guide to the future.Actuaries then use their judgement and expertise to adjust for the various reasons why this is invariablynot the case. In the absence of the application of this judgement and experience, Proxy Methods can bematerially inaccurate. That is not to say that any one Proxy Method is inherently wrong or unsuitable.Just that Proxy Methods cannot be relied upon to provide a meaningful estimate of future claims costswithout appropriate judgement and understanding.


23/58

16

Some of the many reasons why the assumptions underlying Proxy Methods may not hold good arelisted below:

Expected or Plan loss-ratios may be based on flawed assumptions. Few people set out andPlan to write unprofitable business but many of them manage to do so. Plans are generallymore likely to be optimistic than pessimistic.

Individual company experience, of both profitability and claims developments, may be markedlydifferent from the market as a whole. Market benchmarks for loss-ratios or claims developmentfactors may lead to reserves being significantly under- or over- stated.

External legal, or claims environment factors (such as inflation, unemployment, marketcompetitiveness) can change rapidly. For example legal decisions may increase or decreasetypical amounts of injury claims, or worsening unemployment may considerably increase theftfrequencies. New types of claim may also emerge. Past assumptions about claimsdevelopment can become inappropriate in a short amount of time. Benchmark data would notreflect recent experience quickly enough.

Claims development patterns may be significantly changed by internal factors such as newclaims handling guidelines, new approaches to setting case estimates, the speed of payingclaims or work state issues in the claims team. Internally (or externally) derived claimsdevelopment patterns may need adjustment to reflect changes in claims handling processes.

Internal product features may have changed which need to be reflected in any reservingexercise. For example higher or lower excesses or deductibles may dramatically changeaverage costs or the number and speed of reporting of claims.

Underwriters may have changed and the types of business being written may have changed.These changes could invalidate any assumptions about past claims or profitability experience.

There may have been changes to a companys IT systems that mean that claims data is

distorted, or changed, and past trends in claims data are no longer appropriate.

As a generic observation, over-reliance on any one Proxy Method is inappropriate. Each may,at a point in time, produce sensible estimates. However changing circumstances may renderits accuracy and validity of limited use. For this reason no one Proxy Method should be thoughtof as appropriate, rather a range of approaches should be used.

The list of potential pitfalls could go on for many pages. The UK Actuarial Professions Solvency IIBest Estimate

(2)and GRIT

(3)papers, referred to in section 3.5 and for which further details are provided

in Appendix III, provide a more exhaustive list of some of the considerations that may cause ProxyMethods, or any method of setting provisions, to produce misleading results.


24/58

17

APPENDIX I

QUESTIONS WE ASKED CONSULTANCIES ABOUT PROXY METHODS

We asked each of the consultancies described in section 1 the following questions, either over thephone or by way of e-mail questionnaire:

Q1. What proxy methods have you seen in the UK?

Q2. What do you use as reasonableness checks?

Q3. What issues do you see in using Proxy Methods?

Q4. How many companies do you think use Proxy Methods - number and % of market?


25/58

18

APPENDIX II

SAMPLE BENCHMARK INFORMATION FROM LLOYDS OF LONDON

Section 4.4 describes the Lloyds reserve benchmarking exercise. A sample showing the type of report

they produce (excluding the detailed Appendices) is attached.


26/58

19

Agent Reserve

BenchmarkingPackDummy Agent

Market Reserving and Capital


27/58

GI PM EG Report version 5.doc

20

Disclaimer

No responsibility of liability is accepted by the Society of Lloyds, the Council, or any Committee of Board

constituted by the Society of Lloyds or the Council or any of their respective members, officers, or advisors for any

loss occasioned to any person acting or refraining from action as a result of any statement, fact, figure or

expression of belief contained in this document or communication.

The views expressed in the paper are Lloyds own. Lloyds provides the material contained in this document for

general information purposes only. Lloyds accepts no responsibility, and shall not be liable for any loss which may

arise from reliance upon the information provided.


28/58


21

Contents

1 Introduction 4

2 Executive Summary 5

3 Reserve Benchmarking Index 63.1 Gross Reserve Benchmarking Index for YOA 1993 2006 63.2 Net Reserve Benchmarking Index for YOA 1993 2006 7

4 Portfolio Summary 84.1 Market Share by Class of Business 84.2 Net Ultimate Claims and Reserves Analysis 9

5 Premiums and Claims Development Pattern 105.1 Premiums and Claims Development Patterns 10

6 Key Performance indicators (KPIs) 126.1 Reserve Strength KPIs 12

6.2 IBNR Utilisation KPIs 146.3 Reserving over time KPIs 166.4 Quality of Business 186.5 Reinsurance KPIs 206.6 Ultimate Loss Ratio 22

7 Anomalies 23

8 Guide to Exhibits 24

9 Data and Methodology 279.1 Introduction 279.2 The Data 279.3 The Benchmark Portfolio calculation 27

9.4 Numerical Example of Benchmark Calculation 289.5 Deviations, the KPI and Index Calculations 289.6 The Ratio Formulae 29

Appendix A - Definition of Key Performance Indicators 30A.1 Reserve Strength 30A.2 IBNR Utilisation 30A.3 Reserving Over Time 30A.4 Quality of Business 31A.5 Reinsurance 31A.6 Ultimate loss ratio 31

Appendix B - Data Adjustments 32

Appendix C - Reserve Benchmarking Index (Closed Years) 34C.1 Gross Reserve Benchmarking Index for Naturally Closed Years (1993 2003) 34C.2 Net Reserve Benchmarking Index for Naturally Closed Years (1993 2003) 35

Appendix D Detailed KPI Analysis 36D.1 2006 As At Year Summary - Years of Account Grouped by Cycle 36D.2 2006 As At Year Summary - Years of Account Grouped by Naturally Open/Closed Years 37D.3 Detailed Summaries over Time - Years of Account Grouped by Cycle 38D.4 Detailed Summary - Years of Account Grouped by Naturally Open/Closed Years 41

Appendix E List of Classes of Business with Risk Codes 44


29/58


22

1 Introduction

The Agent Reserve Benchmarking Pack has several important purposes. These include:

to allow agents and MRC to gain an understanding of the agents reserve levels relative to the market;

to be an internal tool for agents to help in analysing their reserves and reserve strength;

to bring together comparative analysis carried out by MRC into one document;

to identify agents whose reserving history appears consistently stronger or weaker than average;

to act as a basis for discussion with agents about their reserving;

to provide information to ICA reviewers.

Throughout the pack, data relating to your business is described as Own Portfolio. This includes:

Agent Name Syndicate(s)

Dummy Agent a, b, c

The general approach used in producing the pack is as follows:

The agents Own Portfolio and a Market Portfolio Benchmark were compared. The Market Portfolio

Benchmark is calculated from the market data for all other syndicates adjusted to reflect the underlying

mix of business in the agents Own Portfolio. Details of the construction of the Benchmark are given in

section 9.3;

Key Performance Indicators (KPIs) have been calculated to give indicative measures of various items

regarding reserve strength and nature of the business;

The deviations of the KPIs have been ranked and then combined into various indices to give aconstructive insight into relative reserve strengths for the agents, with the Reserve Benchmarking Index

(RBI) as an overall summary. However, the RBI should be used in conjunction with its various

components.

The data used in this pack is from the 2006 year end Solvency and Reserving Data (SRD) in respect of a

particular agent and the market. Apart from where specifically stated, the data within the pack all relates to

earned business only to avoid unnecessary distortions caused by annual accounting data items.

The pack contains tables and charts detailing the statistics of the agents performance relative to the market

benchmark performance. A guide to the exhibits in the pack is provided in Section 8. The guide contains a short

description of the information in each section of the pack.

Sections 2-6 contain the core analysis work, ranging from a summary level analysis to a more detailed level of

data granularity.

Sections 7-9 contain information concerning KPI anomalies, the guide to exhibits and calculation methods within

the pack.

Finally, the appendices contain definitions of KPIs, details of data adjustments, additional analysis at a more

granular level and details of the business categorisations used within this pack.

MRC contacts are available to discuss the content of the Pack and observations made within it with agents.

Currency Units: All data within this pack is represented in converted m unless otherwise stated. Amounts were

converted to Sterling using the year end exchange rates GBP1 = USD1.96 = CAD2.28 as set out in Market

Bulletin Y3939.

Naturally Closed Years: Some exhibits within this report compare the performance of year of account (YOA)

categorised as naturally closed, MRC defines this as YOA prior to 2004 with 2006, 2005 and 2004 YOA being

categorised as naturally open. Thus some open years are recorded in the naturally closed category.


30/58


23

2 Executive Summary

Key Performance Indicators (KPIs) have been calculated to give indicative measures of various items regarding

reserve strength and the nature of the business. A Reserve Benchmarking Index (RBI) has been constructed

using these KPIs for each YOA on both gross and net of reinsurance basis. These are represented in Chart 2.1.

The quartiles measure the relative performance of the agent concerned against all other agents, 1st

being highestand 4

ththe lowest quartile performance rankings. The RBI quartile for all YOA for both gross and net of

reinsurance basis is shown in table 2.1. This table also contains the corresponding main components of the index.

This index is shown in more detail in Section 3.

Table 2.1 Quartile Ranking of Index

Relative quartile position for the RBI and its components of the Own Portfolio.

Gross Net

RESERVE BENCHMARKING INDEX (RBI)

1. Reserve Strength 2. IBNR Utilisation 3. Reserving Over Time 4. Quality Of Business

Chart 2.1 Reserve Benchmarking Indices (RBI) as at Year end 2006 split by Year of Account

The chart displays the RBI quartiles for each YOA on both gross and net of reinsurance basis.

4th Quartile

3rd Quartile

2nd Quartile

1st Quartile

1993 to

1996

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

YOA

Gross Net


31/58


24

3 Reserve Benchmarking Index

The tables and charts below show the RBI quartiles for the Own Portfolio together with supporting information. The

RBI is derived from four sub-indices; each in turn derived from KPI deviation statistics. These measures are

reported for the period 1993-2006 combined, assessed as at year end 2006. For the KPI within each sub-index

the table shows the Own Portfolio value, the Benchmark value and the deviation between them. The first chart of

each section shows the quartile positions of the RBI and sub-indices for each YOA, and the second chart shows

the same components measured at as dates 2001 to 2006.

More detailed analysis by YOA can be found in section 6 while details of the calculation methodology can be found

in Section 9. The corresponding analysis for naturally closed years (1993-2003) is shown in Appendix C.

3.1 Gross Reserve Benchmarking Index for YOA 1993 2006

Table 3.1.1 Key Performance Indicators (KPIs)

GROSS Quartile

Reser ve Benchmar kin g Index Portfolio Benchmark Deviation

1. Reserve Strength 1.1 Reserve as % Ultimate Claims 31.6% 29.1% 2.5%1.2 IBNR as % Ultimate Claims 12.2% 12.8% (0.6%)

1.3 Case Reserve as a % o f Incurred 22.1% 18.6% 3.4%

1.4 Survival Ratio (years) 2.3 2.3 0.0

2. IBNR Utilisation 2.1 IBNR Burn (1 year) During 2006 50.3% 35.8% (14.5%)2.2 IBNR Burn (1 year) During 2005 47.4% 39.2% (8.1%)

2.3 IBNR Burn (1 year) During 2004 20.6% 30.4% 9.8%

3. Reserving over time 3.1 Ultimate(@now) as % Ultimate(@YOA) 97.3% 98.8% 1.6%3.2 Ultimate(@now) as % Ultimate(@YOA+2) 93.2% 99.4% 6.1%

4. Quality of business 4.1 Paid Loss Ratio 62.9% 60.3% (2.6%)

Ot her KPIs

5. Reinsurance N/A 5.1 % Premium Ceded 32.8% 30.5% 2.3%5.2 RI ULR 97.3% 100.9% (3.6%)

5.3 RI Ult imate as % of Gross Ult imate Claims 34.7% 36.2% (1.5%)

5.4 RI Reserve as % Gross Reserve 28.1% 28.3% (0.1%)

6. Ultimate Loss Ratio N/A 6.1 ULR 91.9% 85.0% (6.9%)

Chart 3.1.1 Gross RBI Index & Components by Year of Account

1st Quartile

2nd Quartile

3rd Quartile

4th Quartile

1993 to

1996

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

YOA

RBI Reserve Strength IBNR Utilisation

Reserving over-time Quality of Business Index RBI All YOA

Chart 3.1.2 Gross RBI Index & Components by "as at Year End"

1st Quartile

2nd Quartile

3rd Quartile

4th Quartile

2001 2002 2003 2004 2005 2006

As at Year


Reserving over-ti me Quali ty of B usines s Index


32/58


25

3.2 Net Reserve Benchmarking Index for YOA 1993 2006

Tables 3.2.1 Key Performance Indicators (KPIs)

Net Quartile

Reser ve Benchmar king Index Portfolio Benchmark Deviation

1. Reserve Strength 1.1 Reserve as % Ulti mate Claims 34.8% 32.7% 2.1%1.2 IBNR as % Ultimate Claims 16.2% 16.1% 0.1%

1.3 Case Reserve as a % of Incurred 22 .3% 19.8% 2.5%

1.4 Survival Ratio (years) 3.1 3.4 (0.2)

2. IBNR Utilisation 2.1 IBNR Burn (1 year) During 2006 44 .5% 29.5% (15.0%)2.1 IBNR Burn (1 year) During 2005 40 .6% 33.5% (7.1%)

2.1 IBNR Burn (1 year) During 2004 24 .0% 28.0% 4.0%

3. Reserving over time 3.1 Ultimate(@now) as % Ultimate(@YOA) 96.5% 94.4% (2.1%)3.2 Ultimate(@now) as % Ultimate(@YOA+2) 100.7% 99.5% (1.3%)

4. Quality of business 4.1 Paid Loss Ratio 58.2% 52.5% (5.7%)

Ot her KPIs

6. Ultimate Loss Ratio N/A 6.1 ULR 89.2% 78.0% (11.3%)

Chart 3.2.1 Net RBI Index & Components by Year of Account

4th Quartile

3rd Quartile

2nd Quartile

1st Quartile

1993 to

1996

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

YOA


Reserving over-time Quality of Business Index RBI All YOA

Chart 3.2.2 Net RBI Index & Components by "as at Year End"

4th Quartile

3rd Quartile

2nd Quartile

1st Quartile

2001 2002 2003 2004 2005 2006As at Year


Reserving over-time Qua li ty o f Bus iness Index


33/58


26

4 Portfolio Summary

4.1 Market Share by Class of Business

The tables and charts below show the mix of classes of business and the market share of the Own Portfolio for

each year of account measured by the gross cumulative written premium as at year end 2006.

Table 4.1.1 Own Portfolio Mix by Lloyds High-Level Class of Business

This table shows for each YOA, the mix of the agents business expressed by the Lloyds high level class of business.

YOAProperty

(D&F)

Property

TreatyCasualty

Casualty

TreatyAviation Marine Energy UK Motor

Overseas

Motor

Accident

& Health

1993 11.4% 7.5% 14.4% 0.5% 5.4% 31.0% 10.3% 6.0% 0.4% 13.1%

1994 4.9% 5.0% 5.0% 0.5% 7.5% 36.8% 14.0% 11.2% 0.2% 14.8%

1995 6.3% 4.4% 7.5% 0.8% 5.6% 49.9% 15.8% 9.5% 0.2% 0.0%

1996 7.0% 5.4% 9.3% 0.8% 4.6% 56.0% 16.7% 0.0% 0.0% 0.1%

1997 10.2% 4.4% 22.9% 1.1% 6.6% 36.4% 13.8% 2.1% 0.2% 2.3%

1998 10.2% 7.5% 7.4% 1.6% 10.1% 39.6% 15.0% 6.0% 0.7% 2.0%

1999 11.0% 8.4% 8.2% 1.5% 10.6% 33.1% 13.9% 7.1% 2.2% 4.0%

2000 15.8% 2.8% 9.3% 0.8% 5.6% 33.1% 15.0% 7.9% 3.9% 5.9%

2001 17.3% 5.0% 12.4% 0.8% 4.3% 32.3% 10.6% 7.4% 3.7% 6.2%2002 28.6% 7.7% 10.8% 3.6% 6.5% 26.3% 5.3% 4.9% 1.8% 4.4%

2003 24.5% 10.5% 14.2% 4.0% 9.0% 23.9% 6.7% 0.0% 0.5% 6.8%

2004 19.1% 24.2% 10.3% 4.7% 9.4% 18.6% 6.6% #N/A 0.3% 6.8%

2005 16.8% 30.5% 9.3% 6.8% 7.4% 16.0% 7.0% #N/A 0.5% 5.7%

2006 13.1% 35.0% 6.7% 7.5% 7.3% 18.8% 7.4% #N/A 0.5% 3.7%

Chart 4.1.1 - Own Portfolio Mix by Lloyds High-level Class of Business

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

YOA

%O

wnPortfolio

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

MarketShare

Property (D&F) Property Treaty Casualty Casualty Treaty

Aviation Marine Energy UK Motor

Overseas Motor Accident & Health Market Share by GCWP

Table 4.1.2 Own Portfolio Market Share Position by Lloyds High-Level Class of Business

This table shows the classes of business for which the Own Portfolio has significant market share for each YOA. It also shows

the Own Portfolio aggregate market share across all classes of business.

YOA

Market

Share by

GCWP

Property

(D&F)

Property

TreatyCasualty

Casualty

TreatyAviation Marine Energy UK Motor

Overseas

Motor

Accident &

Health

1993 9.6% TOP5 TOP5 TOP5 TOP10 TOP5 TOP5 TOP5 TOP10 TOP20 TOP5

1994 5.4% TOP20 TOP20 TOP20 TOP10 TOP10 TOP5 TOP5 TOP10 TOP5

1995 3.4% TOP20 TOP20 TOP20 TOP10 TOP20 TOP5 TOP5 TOP10

1996 2.4% TOP20 TOP20 TOP10 TOP20 TOP5 TOP10


1998 2.8% TOP20 TOP20 TOP5 TOP10 TOP5 TOP5 TOP20




2002 4.5% TOP5 TOP20 TOP20 TOP5 TOP10 TOP5 TOP20 TOP10 TOP10 TOP10


2004 5.5% TOP10 TOP5 TOP20 TOP5 TOP5 TOP5 TOP5 #VALUE! TOP20 TOP5

2005 6.4% TOP10 TOP5 TOP20 TOP5 TOP5 TOP5 TOP5 #VALUE! TOP20 TOP52006 6.9% TOP10 TOP5 TOP20 TOP5 TOP5 TOP5 TOP10 #VALUE! TOP10 TOP5


34/58


27

4.2 Net Ultimate Claims and Reserves Analysis

The tables and charts below show the components of the net ultimate claims and reserves for each YOA, and how

the reserves are split by class of business.

Table 4.2.1 Components of Agents Net Ultimate Claims

This table shows the components of the Own Portfolio net ultimate claims for each YOA (in converted million).

1993 to 1996 22 571

1997 10 137

1998 17 160

1999 28 233

2000 52 277

2001 44 308

2002 35 182

2003 42 210

2004 88 474

2005 267 721

2006 42 184

281 173

16 127

2,253 646Total

39108

59

95291

137

216

11

(2)

Outstanding

108

194

PaidYOA

6

230

551

120

556 3,456

Ultimate

8

35

6

IBNR

Note: Ultimate here is the ultimate claims arising from earned premiums. No allowance has been made for claims arising from unearned premium.

Chart 4.2.1 Net Reserves by High Level Line

This chart shows the net reserves for YOA 1993 to 2006 as at year end 2006 split within the pie by Lloyds high

level class of business and the largest component of the first pie is analysed into Lloyds Low Level classes of

business in the column chart

Marine 18.5%

Property (D&F) 16.4%

Property Treaty 14.8%

Casualty Treaty 9.9%

Energy 8.8%Aviation 6.1%

Accident & Health 3.3%

Other 1.1%

NM General Liability (US direct)

6.6%

Professional Indemnity (US)

5.5%

Medical Malpractice 2.6%

Professional Indemnity (non-US)

1.6%

Employers Liability/ WCA (US)

1.4%

Other 3.5%

Casualty 21.1%


35/58


28

5 Premiums and Claims Development Pattern

5.1 Premiums and Claims Development Patterns

The tables and charts in this section show the development of the net ultimate claims and the net ultimate premiums for

each year of account from the Own Portfolio data. Both the ultimate premium and claims positions given in section 5

include unearned provisions.

Table 5.1.1 Net Ultimate Premium Development (m)

This table shows the net ultimate premium for each YOA as at each year end. This gives an indication of how well the

future premiums were estimated at each development year and the level of confidence in the current estimate.

YOA 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

1993 389 455 458 458 458 459 460 459 450 451 451 450 449 449

1994 227 254 267 269 270 270 266 266 267 267 266 265 265

1995 126 148 173 174 173 173 172 173 173 172 172 172

1996 47 96 102 107 107 107 107 107 106 107 1071997 70 105 177 178 178 180 180 177 178 178

1998 71 112 112 111 111 108 107 108 108

1999 136 138 137 152 149 148 148 148

2000 147 175 181 170 174 180 175

2001 219 243 230 224 225 230

2002 329 331 341 336 336

2003 430 385 384 380

2004 514 586 567

2005 438 495

2006 562

'As at' Year

Table 5.1.2 - Net Ultimate Claims Development (m)

This table shows the net ultimate claims for each YOA as at each year end. This gives an indication of how well the

claims reserves were estimated at each development year. Decreasing ultimate values can indicate previous strong

reserves or a weakening in reserving basis.

YOA 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

1993 59 146 330 316 311 255 272 282 266 269 281 277 285 267

1994 60 175 176 167 166 165 166 166 167 161 161 166 161

1995 86 83 100 93 87 85 86 84 81 80 79 80

1996 29 62 77 73 73 71 70 70 70 69 69

1997 53 88 154 164 181 147 131 130 132 133

1998 75 130 138 147 152 156 158 153 160

1999 153 166 204 227 231 227 231 233

2000 150 244 262 255 282 276 278

2001 324 332 308 292 291 308

2002 246 219 172 164 182

2003 371 235 211 212

2004 480 482 478

2005 788 753

2006 443

'As at' Year


36/58


29

Chart 5.1.1 - Net Ultimate Premium Development

This chart shows the development of the Own Portfolio Net ultimate premium for each YOA. The figures used in this

chart are shown in Table 5.1.1

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4D e v e l o p m e n t Y e a r

m

1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9

2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6

Chart 5.1.2 - Net Ultimate Claims Development

This chart shows the development of the Own Portfolio Net ultimate claims for each YOA. The figures used in this chart

are shown in Table 5.1.2

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

8 0 0

9 0 0

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4

D e v e l o p m e n t Y e a r

m

1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9

2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6


37/58


30

6 Key Performance indicators (KPIs)

The tables within this section show a range of reserving KPIs for the Own Portfolio and the Benchmark Portfolio for each

YOA. In tables that show deviations between the Own Portfolio and the Benchmark Portfolio these are colour coded with

green indicating a favourable deviation and red an unfavourable deviation.

For details of the measures and formulas, used to calculate the results below, please refer to Section 9 and Appendix A.

6.1 Reserve Strength KPIs

Table 6.1 - Reserve Strength Percentile Rank

YOA 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1993 to 2006

Percentile

Rank

Percentile

Rank

Gross

Net

Table 6.1.1 - Reserve as % of Ultimate Claims

YOA 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1993 to 2006

Portfolio 4.0% 3.1% 3.1% 6.8% 7.3% 9.4% 18.1% 28.1% 29.3% 38.4% 46.4% 37.6% 52.9% 91.8% 31.6%

Benchmark 3.2% 3.6% 3.8% 5.6% 10.3% 8.6% 11.6% 20.1% 26.3% 33.1% 42.2% 34.5% 50.9% 88.2% 29.1%

Deviation 0.8% (0.5%) (0.7%) 1.2% (3.0%) 0.8% 6.5% 8.0% 2.9% 5.4% 4.2% 3.1% 2.0% 3.7% 2.5%

Portfolio 4.7% 3.0% (4.1%) 8.2% 11.8% 14.0% 16.8% 21.8% 25.4% 40.5% 48.4% 38.6% 61.0% 91.5% 34.8%

Benchmark 3.1% 3.6% 4.0% 7.0% 12.5% 11.3% 15.4% 19.4% 28.6% 32.6% 43.4% 38.7% 60.8% 87.5% 32.7%

Deviation 1.5% (0.6%) (8.2%) 1.1% (0.7%) 2.7% 1.4% 2.5% (3.2%) 7.9% 5.0% (0.2%) 0.2% 4.1% 2.1%

Gross

Net

Table 6.1.2 - IBNR as % of Ultimate Claims

YOA 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 1993 to 2006

Portfolio 0.4% 0.2% 0.7% 1.6% 1.3% 2.0% 5.8% 2.6% 9.3% 17.8% 24.8% 16.8% 18.1% 69.0% 12.2%

Benchmark 0.8% 0.9% 0.9% 1.6% 2.6% 2.4% 3.6% 5.2% 9.0% 14.9% 22.0% 16.5% 19.3% 67.5% 12.8%

Deviation (0.4%) (0.7%) (0.2%) 0.0% (1.3%) (0.4%) 2.2% (2.6%) 0.3% 2.9% 2.9% 0.3% (1.1%) 1.5% (0.6%)

Portfolio 0.4% 0.0% 1.1% 2.4% 2.0% 3.6% 4.9% 3.3% 11.3% 21.3% 28.4% 20.0% 23.9% 68.8% 16.2%

Benchmark 1.0% 0.9% 1.1% 2.3% 3.8% 3.8% 5.7% 6.7% 11.4% 15.5% 21.8% 20.0% 26.5% 66.4% 16.1%

Deviation (0.5%) (0.8%) (0.0%) 0.1% (1.8%) (0.2%) (0.8%) (3.5%) (0.1%) 5.8% 6.5% (0.0%) (2.6%) 2.4% 0.1%

Gross

Net

Table 6.1.3 Case reserves as % of Incurred Claims

Documents

Actuarial Best Estimates and Proxy Methods