INTRODUCTION TO THE KEY ANALYTICAL CONCEPTS REFLECTED WITHIN THE BIBA LITMUS TEST REPORT (the BLTR)
The BLTR service is not limited to the insurer reports themselves; it is also a source of educational and informational material for BIBA members, such that they can use the reports as effectively as possible and more widely to support member understanding of the consideration of insurer financial strength.
This introductory guide covers the main concepts behind the ratio analysis of an insurer and the key considerations the member should keep in mind when reviewing an insurer’s BLTR.
Some of the subjects covered here are also described in more depth under other BLTR tabs.
The meaning and purpose of ‘risk capital’ (shareholders’ funds)
There are many issues to consider in assessing an insurer’s prospective financial strength, but at any given moment, its current financial strength can be regarded as the amount of risk capital it holds relative to the risks to that capital.
Risk capital in insurer credit analysis essentially means the excess value of assets over liabilities*, as in the illustration Fig. 1 below
(*Some accounting approaches define the shareholders’ funds themselves as a type of liability – i.e. money the firm holds that belongs to the shareholder, but that is basically just accounting semantics).
Thus ‘assets minus liabilities’ = shareholders’ funds
Many different terms are used to describe this amount in practice. In the BIBA Litmus Test Report (BLTR) we use the term ‘shareholders’ funds’ as this is among the most widely adopted terms. The use of this single term is for convenience: it is not intended to indicate that any given insurer covered within the BLTR is necessarily shareholder owned (we would use the same term for a mutual insurer).
It’s a simple concept but can reflect greater complexity when the specific make-up of the reported assets and liabilities is considered. For example, while ‘goodwill’ can be a feature of an insurer’s reported assets, under both Solvency II and among the major credit rating agencies, this amount is generally ‘disallowed’ when calculating the capital notionally available to support risk taking. We repeat this approach within the BLTR when calculating ‘adjusted shareholders’ funds’.
We explain this further under the ‘Data adjustments’ tab.
The relevance of shareholders’ funds within insurer credit analysis is therefore that these represent the capital notionally available to support underwriting or asset risk (the ‘risk capital’). Central to such an analysis is forming a perspective on the scale of the available risk capital (the shareholders’ funds) relative to the financial risks taken with it (both in terms of their scale and their nature).
In the BLTR the capital ratios are intended to support the forming of that perspective.
The purpose of the financial ratios
Financial analysts of all types (credit, equity, regulatory and others) use financial ratios as key tools in the analysis process.
A simple and widely known example of this can be seen in mortgage lending to private individuals. Typically, the mortgage lender will have acceptable ratio ranges for issues such as the borrower’s income relative to the size of the loan/repayments and the size of the loan relative to the deposit/value of the house.
While the finances of a non-life insurer are clearly far more complex than those of a private individual buying a property, the underlying concepts around the use of analytical ratios are similar, and we use this ‘purchasing a house via a mortgage’ example as an illustrative comparison at various points in this guide
Capital ratios relate the sources of risk to the shareholders’ funds (see below) to their scale. That is the same principal as comparing the total value of a house to the size of an outstanding mortgage.
In the housing market the difference between the market value of a house and the outstanding mortgage is commonly known as the ‘homeowner’s equity’. All other things being equal, the lower the amount of homeowner’s equity relative to the value of the house, the riskier the mortgage loan is for the lender (since this represents the ‘excess collateral’ the lender has in the event it had to repossess the house). This risk is illustrated in Figures 2 and 3 below.
A house is worth £300k the day it is purchased with an interest only £275k mortgage and a £25K deposit.
A year later the house is worth only £250k. As the mortgage is interest only it remains at £275K.
So the homeowner’s equity is therefore now minus £25k and that represents the shortfall in the collateral the lender has available to it should it need to repossess.
A ‘homeowner’s equity’ in their house at any given time is conceptually the equivalent to the shareholders’ funds for a non-life insurer. Policyholders with a current or future claim are collectively conceptually the equivalent of the mortgage lender. The initial £25K deposit is conceptually the equivalent to the paid-in capital of an insurer.
For a non-life insurer, the risks to its shareholders’ funds most commonly come from three sources:
Our homeowner example therefore is analogous to the ‘asset risk’ for a non-life insurer: that the value of what they own (their assets) falls below what they owe (their liabilities). Typically for a non-life insurer what they ‘owe’ is primarily their ‘technical reserves’ (technical reserves are the loss/claims reserves, plus an amount called the unearned premium reserve that adjusts for the fact that the insurer has not yet fully earned all the policy premium on any policy that has not expired at its balance sheet date).
The notional impact of reduced asset values or increased required reserves is illustrated in figures 4 and 5
The value of assets held by the insurer is £100m higher than the reserves it needs to hold for claims and unearned premiums (collectively known as the technical reserves).
BUT if, for example, the assets held by the insurer drop in value by £50m and its actuaries decide it actually needs £70m more to be held in its loss reserves because of adverse claims development, it would become insolvent by £20m (as illustrated in Fig. 5 below).
The value of assets held by the insurer is £20M less than its required reserves. The insurer is insolvent notwithstanding the potential that some of the reserves it is holding for future claims may take years, or even decades, to be needed.
Performance (earnings) ratios
For the mortgage holder in our example above, finding that they have ‘negative equity’ in the house (that their mortgage is greater than the current value of the house) is, while painful, not necessarily a fundamental problem with their lender: assuming the borrower can keep making the mortgage payments.
For an insurer however the conceptual equivalent of ‘negative equity’ (liabilities exceeding assets) is ‘insolvency’ (as illustrated in Fig. 5 above).
So the scale of shareholders’ funds relative to the sources of risk to those funds is fundamental to the analysis of an insurer’s credit profile (e.g. as observed within the capital ratios described in 3a).
Why though would ‘performance’ ratios be seen as relevant by insurance credit analysts?
This reflects the fact that, since most policyholders have a future credit exposure to their insurer (a potential future claim) rather than a current credit exposure (a current claim), how the current level of shareholders’ funds evolves in the future is very important. Insurance credit analysts have learnt that the most important general leading indicator of strong future capital is strong operating performance (with retained profits seen as the best source of the future growth of shareholders’ funds).
The BLTR also references a ‘reinsurance usage’ and a ‘liquidity’ ratio.
The purpose of the former is to get an insight into how much the business model of the insurer relies on reinsurance via the percentage of gross written premium that it cedes.
While for almost all non-life insurers buying some reinsurance is fundamental to managing their risk appetite and credit risk profile, a proportionately large level of cessions could indicate that the insurer’s business model is materially dependent on its access to reinsurance cover (in other words that the insurer is meaningfully exposed to adverse changes in the price and/or availability of reinsurance capacity).
Liquidity is another analytical issue that can be related to the mortgage holder example shown in section 2a. Having sufficient liquid assets (cash or assets readily exchangeable for cash) is fundamental to meeting mortgage obligations in a timely way. Similarly, for a non-life insurer, there is the need not just to be solvent but to maintain strong liquidity to pay claims.
The significance of size, diversification and market position
The scale of a non-life insurer’s business can have a very important impact on its credit risk profile.
Diversification across risk sources (both within lines of business and asset classes and across both) has a major impact on views of both apparent capital strength and the potential riskiness of earnings.
To return to the mortgage holder example, simply owning a house and having no other assets provides no diversification of asset risk. A large non-life insurer can, however, have a very highly diversified portfolio of investments across bonds, equities, real estate etc.
Similarly, the greater the scale of the underwriting operation, the greater the opportunity for class/line of business, policyholder and geographic diversification.
Smaller insurers, by contrast, typically have less opportunity to diversify both asset and underwriting risks.
Generally, all other things being equal, lower levels of underwriting and/or asset diversification equate to higher risk. In addition the strength of an insurer’s market position can often be a function of size. A stronger market position can support pricing power with both insurance buyers and brokers – and when negotiating terms with reinsurers.
Hence, from a credit risk profile perspective, the smaller the insurer the greater the extent to which stronger ratios would typically be desirable; such as when a successful niche underwriting strategy allows a small insurer to achieve both strong operating performance and – via retained profits – strong capital ratios, helping to offset a lack of diversification.
The calculation and purpose of the benchmark categories
To provide BLTR users with a context for considering individual ratio outcomes, we compare each to a UK non-life market benchmark calculated from a cohort of 50 selected UK non-life insurers (the benchmark cohort). For each ratio the cohort list is then ranked from the ‘best/strongest’ to the ‘worst/weakest’ outcome.
The cohort results are then split into 5 categories via 4 benchmarks for each ratio.
The benchmarks are taken as follows for best/strongest to worst/weakest:
Category 2 = ratios worse than the 10% benchmark but better than or equal to the 30% benchmark
Category 3 = ratios worse than the 30% benchmark but better than or equal to the 70% benchmark
Category 4 = ratios worse than the 70% benchmark but worse than or equal to the 90% benchmark
Category 5 = ratios worse than the 90% benchmark
The reason for the wider percentile ranges in the middle benchmarks is to allow for the fact that it would be expected that there is some clustering around the mean (50%) in the benchmark cohort. Hence the average difference in any ratio between adjacently ranked members of Category 3 would be expected to be lower than that seen in categories 1 or 5.
For each insurer covered by the BLTR we then identify which of the 5 categories each of its ratios falls into.
Inevitably for any new financial year the availability of the accounts for all the benchmark cohort members – such that the new year’s benchmarks can be calculated – will lag the availability of some of the covered insurers’ accounts for that year.
Waiting for an updated benchmark calculation before publishing updated individual BLTRs would not be desirable because members would be looking at unnecessarily outdated data on the covered insurer.
Hence updated insurers are benchmarked against the prior year benchmark percentiles until the latest year becomes available. At which point all the BLTRs are recalculated and updated.
To limit year-on-year volatility within the benchmark levels these are averaged over the last 3 years. Hence the 2014 financial year benchmarks reflect those for the individual years of 2014, 2013 and 2012, added together and divided by 3. 2015 would then be calculated by combining the 2015 individual benchmarks with those for the years 2014 and 2013, and so on as the years move forward.
This also helps mitigate the impact of changes over time in the insurers included in the benchmark cohort.
More details on the benchmark cohort selection protocols employed, the benchmark calculations and the benchmark outcomes are available under the ‘ Categories’ tab within the BLTR.
Important limitations of ratio analysis
In the more detailed explanations of the different ratios in the BLTR (see the ‘Ratio Guide’ tab) we examine further how seemingly positive or negative ratio outcomes might actually not be that clear cut, as well as other issues the member should keep in mind related to that ratio.
In essence, while the BLTR ratios have been selected because of what they typically help indicate, any given ratio may not be representative in a particular case.
Reviewing a range of ratios across a non-life insurer’s financial profile, as we show within the BLTR, is how credit analysts partially address this. Nonetheless that can never fully remove the potential that the non-life insurer’s data overall (and hence for example ratios derived from them such as those shown within the BLTR) is in practice not substantially representative of its credit risk profile.
Assessing this is a matter for a member’s judgement and the educational and informational content provided alongside the BLTR is in part to support the member in doing so.
Sourcing and standardisation of the financial data
The data used within the BLTR to calculate the ratios and benchmarks is provided by A.M. Best Company Ltd. (Best’s). In addition to being globally recognised as a specialist insurer rating agency, A.M. Best is also a global leader in insurance company financial statement data capture and standardisation (covering many thousands of insurers globally that it does not rate).
The standardisation process involves Best’s applying its analytical expertise to standardise the originally reported accounts data both within a domicile, and then across domiciles to provide as globally consistent a data set as can be achieved.
More details on Best’s data are shown under the A.M. Best tab or can be obtained from Best’s directly.
The difference between reported and adjusted shareholders’ funds
Insurance credit analysts do not typically consider all assets to be valid for the purposes of calculating risk capital (the shareholders’ funds viewed as available to support risk-taking activity). Disallowing an asset has the effect of reducing the ‘reported’ shareholders’ funds (RSF) by the amount of that asset to create an ‘adjusted’ shareholders’ funds (ASF) outcome.
Conversely, there can be situations where positive adjustments are made to the amount of reported shareholders’ funds.
The details and purpose of these changes are discussed further in the ‘Data Adjustments’ tab.
However, it is particularly important for members to consider any adjustments made to the RSF in the light of the individual insurer being looked at. Accordingly, the BLTR provides the relevant ratios (the capital ratios) and their overall benchmark calculations both with the adjustments (the ‘ASF’ box) and without (the ‘RSF’ box)
Reviewing the BLTR in the context of the audited accounts
The BLTR is designed to help members in the process of making a judgement about an unrated insurer’s credit profile via the calculation of key ratios from the audited data as captured and published by A.M. Best. To help that evaluation the ratios are set in the context of the benchmark levels for the non-life UK market calculated by Litmus along with the informational and educational content of the BLTR.
However, this should not be seen as a substitute for a review of the insurer’s accounts, but rather a supporting tool to facilitate such a review.
A number of the issues we highlight for BIBA members to consider when using the BLTR may well be further informed by, or addressed within, the insurer’s accounts.
Loss reserve adequacy and longer tail business
When using data from the accounts of a non-life insurer, the adequacy of its loss (or claims) reserves (reserves for claims reported but not yet paid and for claims incurred but not yet reported) is a crucial consideration.
To return to our mortgage example, an insurer that books insufficient loss reserves is the equivalent of a homeowner’s outstanding mortgage being shown as lower than it truly is.
As Figures 4 and 5 shown in 3a illustrate, under-reserving would overstate the value of the reported shareholders’ funds and hence the strength of the capital ratios.
However, it is extremely difficult to evaluate the potential for this from the publicly available data on most non-life insurers. Some insurers may comment on whether they have recognised ‘adverse development’ (reserves from prior years being insufficient) in their accounts but in theory once a shortfall has been recognised they should have restored reserves to the required level.
The longer the ‘tail’ of the underwriting portfolio of the insurer the greater the potential there can be for under- (or over-) reserving (over-reserving has the reverse impact, making capital ratios look weaker than they truly are). That is because longer tail business tends to have a greater degree of uncertainty around what the final claims (and hence current reserve requirements) will actually be.
Under- or over-reserving can result from an insurer’s management being overly optimistic or pessimistic about future claims development. But they can also result from changes in the claims environment that take place after the business is written but before the claims-causing event is recognised (such as the emergence of an industrial disease like asbestosis).
The ratios within BLTR help set the overall context within which the impact of under- or over-reserving can be considered, but a direct discussion with the insurer by the member on this subject might be necessary if the member is concerned that reserving may not be adequate. One approach can be to request that the insurer provides the member with the views of an independent actuary on its reserve adequacy.
The selection of covered insurers
BIBA selects insurers to be included in the BLTR on the basis that they are active in the UK non-life market and do not hold a ‘financial strength rating*’, or equivalent, from one of the main credit rating agencies generally recognised within the UK non-life market (A.M. Best, Fitch, Moody’s and Standard & Poor’s).
Only non-life insurers are covered by the BLTR. In the event an insurer BIBA wishes to include is ‘very largely’ a non-life company that also writes a modest degree of life business, Litmus will review whether the report structure remains sufficiently applicable. In such a case however members should be conscious that the life business would inevitably reduce the applicability of the BLTR to some degree.
Inclusion of any insurer is subject to A.M. Best being able to source the audited accounts and that Best is able to capture sufficient data from these.
*The term ‘financial strength’ (or sometimes ‘insurer financial strength’’) is typically used by the main rating agencies when assigning a rating from the perspective of a policyholder’s credit risk with the insurer. An insurer that issues bonds may well have a rating covering these but at a different (generally lower) level. It is the ‘financial strength’ (policyholder credit risk) rating this is most relevant to broker considerations of market security.
The concept of the ‘stand-alone’ insurer profile used within the BLTR
The data used in the BLTR is the consolidated data of the named legal entity (both for the covered insurers and in the calculation of the benchmarks). This means that the BLTR reflects the insurer itself and its consolidated subsidiaries (if it has any) but not any aspect of the profile of a wider parent group, if the insurer is part of one. Among credit analysts this leads to a perspective known as ‘stand-alone’.
Hence any strengths or weaknesses of a wider group are not reflected in the insurer’s entry within the BLTR. In practice a financially strong wider group might well support an insurance subsidiary in the event of its financial duress; conversely a group with financial problems – or with growth ambitions elsewhere needing funding – might become a drain on the resources of an otherwise healthy insurance subsidiary.
The potential for either scenario however is outside the scope of a factual report based on audited data such as the BLTR. Consideration of this requires at a minimum a credit analysis of the wider group, an understanding of its strategy and how the insurance subsidiary fits into that, and – to the extent disclosed – the nature of any financial commitments with respect to supporting the subsidiary that the wider group has made to the insurance regulators in the subsidiary’s domicile.
Investments in – or loans made to – other parts of a wider parent group (including the parent itself) are disallowed within the BLTR when calculating the Adjusted Shareholders Funds (ASF) amount (see the Data Adjustments tab) since these may be used for other risk taking activity within the wider group.
to be reflected in the ratios. This is the approach typically adopted by rating agencies and other insurance credit analysts. However, it should be borne in mind that there may be regulatory or other impediments to the movement of capital between an insurer and its subsidiaries.
The importance of the members’ own knowledge of market conditions, the insurer’s areas of operation and its pricing
A key consideration for optimal BLTR use is a member’s own business knowledge of an insurer’s operations: in particular as it relates to the insurer’s reported profitability, underwriting quality, riskiness of its business lines and reserve adequacy (see section 9), all of which can provide a crucial context to interpreting the factors shown on that insurer within the BLTR.
Hence a member’s own direct knowledge and experience of the state of the market and the robustness of an insurer’s pricing within its business lines (and/or associated terms and conditions) can be a very important source of insight. A member will also often see evidence of the quality of an insurer’s risk selection approach.
Of course a judgment needs to be made as to whether what the member is aware of is representative of the insurer’s wider operations, but discussions with other brokers can help.
Thus, if an insurer is materially cheaper* than alternative carriers (normally a high profile risk factor for insurance credit analysts assuming at least normal levels of price competition within the market overall), the member may wish to consider the following:
*In terms of headline rate and/or ‘coverage’ and other terms and conditions.