Category Archives: Insurance Models

Beautiful Models

It has been a while since I posted on dear old Solvency II (here). As highlighted in the previous post on potential losses, the insurance sector is perceived as having robust capital levels that mitigates against the current pricing and investment return headwinds. It is therefore interesting to look at some of detail emerging from the new Solvency II framework in Europe, including the mandatory disclosures in the new Solvency and Financial Condition Report (SFCR).

The June 2017 Financial Stability report from EIOPA, the European insurance regulatory, contains some interesting aggregate data from across the European insurance sector. The graph below shows solvency capital requirement (SCR) ratios, primarily driven by the standard formula, averaging consistently around 200% for non-life, life and composite insurers. The ratio is the regulatory capital requirement, as calculated by a mandated standard formula or a firm’s own internal model, divided by assets excess liabilities (as per Solvency II valuation rules). As the risk profile of each business model would suggest, the variability around the average SCR ratio is largest for the non-life insurers, followed by life insurers, with the least volatile being the composite insurers.

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For some reason, which I can’t completely comprehend, the EIOPA Financial Stability report highlights differences in the SCR breakdown (as per the standard formula, expressed as a % of net basic SCR) across countries, as per the graph below, assumingly due to the different profiles of each country’s insurance sector.

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A review across several SFCRs from the larger European insurers and reinsurers who use internal models to calculate their SCRs highlights the differences in their risk profiles. A health warning on any such comparison should be stressed given the different risk categories and modelling methodologies used by each firm (the varying treatment of asset credit risk or business/operational risk are good examples of the differing approaches). The graph below shows each main risk category as a percentage of the undiversified total SCR.

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By way of putting the internal model components in context, the graph below shows the SCR breakdown as a percentage of total assets (which obviously reflects insurance liabilities and the associated capital held against same). This comparison is also fraught with difficulty as an (re)insurers’ total assets is not necessarily a reliable measure of extreme insurance exposure in the same way as risk weighted assets is for banks (used as the denominator in bank capital ratios). For example, some life insurers can have low insurance related liabilities and associated assets (e.g. for mortality related business) compared to other insurance products (e.g. most non-life exposures).

Notwithstanding that caveat, the graph below shows a marked difference between firms depending upon whether they are a reinsurer or insurer, or whether they are a life, non-life or composite insurer (other items such as retail versus commercial business, local or cross-border, specialty versus homogeneous are also factors).

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Initial reactions by commentators on the insurance sector to the disclosures by European insurers through SFCRs have been mixed. Some have expressed disappointment at the level and consistency of detail being disclosed. Regulators will have their hands full in ensuring that sufficiently robust standards relating to such disclosures are met.

Regulators will also have to ensure a fair and consistent approach across all European jurisdictions is adopted in calculating SCRs, particularly for those calculated using internal models, whilst avoiding the pitfall of forcing everybody to use the same assumptions and methodology. Recent reports suggest that EIOPA is looking for a greater role in approving all internal models across Europe. Systemic model risk under the proposed Basel II banking regulatory rules published in 2004 is arguably one of the contributors to the financial crisis.

Only time will tell if Solvency II has avoided the mistakes of Basel II in the handling of such beautiful models.

The Big Wind

With four US hurricanes and one earthquake in current times, mother nature is reminding us homo-sapiens of her power and her unpredictability. As the massive Hurricane Irma is about to hit Florida, we all hope that the loss of life and damage to people’s lives will be minimal and that the coming days will prove humane. Forgive me if it comes across as insensitive to be posting now on the likely impact of such events on the insurance industry.

For the insurance sector, these events, and particularly Hurricane Irma which is now forecast to move up the west coast of Florida at strength (rather the more destruction path of up the middle of Florida given the maximum forces at the top right-hand side of a hurricane like this one), may be a test on the predictive powers of its models which are so critical to pricing, particularly in the insurance linked securities (ILS) market.

Many commentators, including me (here, here and here are recent examples), have expressed worries in recent years about current market conditions in the specialty insurance, reinsurance and ILS sectors. On Wednesday, Willis Re reported that they estimate their subset of firms analysed are only earning a 3.7% ROE if losses are normalised and reserve releases dried up. David Rule of the Prudential Regulatory Authority in the UK recently stated that London market insurers “appear to be incorporating a more benign view of future losses into their technical pricing”, terms and conditions continued to loosen, reliance on untested new coverages such as cyber insurance is increasing and that insurers “may be too sanguine about catastrophe risks, such as significant weather events”.

With the reinsurance and specialty insurance sectors struggling to meet their cost of capital and pricing terms and conditions being so weak for so long (see this post on the impact of soft pricing on risk profiles), if Hurricane Irma impacts Florida as predicted (i.e. on Saturday) it has the potential to be a capital event for the catastrophe insurance sector rather than just an earnings event. On Friday, Lex in the FT reported that the South-East US makes up 60% of the exposures of the catastrophe insurance market.

The models utilised in the sector are more variable in their output as events get bigger in their impact (e.g. the higher the return period). A 2013 post on the variation in loss estimates from a selected portfolio of standard insurance coverage by the Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) illustrates the point and one of the graphs from that post is reproduced below.

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Based upon the most recent South-East US probable maximum losses (PML) and Atlantic hurricane scenarios from a group of 12 specialty insurers and reinsurers I selected, the graph below shows the net losses by return periods as a percentage of each firm’s net tangible assets. This graph does not consider the impact of hybrid or subordinate debt that may absorb losses before the firm’s capital. I have extrapolated many of these curves based upon industry data on US South-East exceedance curves and judgement on firm’s exposures (and for that reason I anonymised the firms).

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The results of my analysis confirm that specialty insurers and reinsurers, in aggregate, have reduced their South-East US exposures in recent years when I compare average figures to S&P 2014 data (by about 15% for the 1 in 100 return period). Expressed as a net loss ratio, the average for a 1 in 100  and a 1 in 250 return period respectively is 15% and 22%. These figures do look low for events with characteristics of these return periods (the average net loss ratio of the 12 firms from catastrophic events in 2005 and 2011 was 22% and 25% respectively) so it will be fascinating to see what the actual figures are, depending upon how Hurricane Irma pans out. Many firms are utilising their experience and risk management prowess to transfer risks through collaterised reinsurance and retrocession (i.e. reinsurance of reinsurers) to naïve capital market ILS investors.

If the models are correct and maximum losses are around the 1 in 100 return period estimates for Hurricane Irma, well capitalized and managed catastrophe exposed insurers should trade through recent and current events. We will see if the models pass this test. For example, demand surge (whereby labour and building costs increase following a catastrophic event due to overwhelming demand and fixed supply) is a common feature of widespread windstorm damage and is a feature in models (it is one of those inputs that underwriters can play with in soft markets!). Well here’s a thought – could Trump’s immigration policy be a factor in the level of demand surge in Florida and Texas?

The ILS sector is another matter however in my view due to the rapid growth of the private and unregulated collateralised reinsurance and retrocession markets to satisfy the demand for product supply from ILS funds and yield seeking investors. The prevalence of aggregate covers and increased expected loss attachments in the private ILS market resembles features of previous soft and overheated retrocession markets (generally before a crash) in bygone years. I have expressed my concerns on this market many times (more recently here). Hurricane Irma has the potential to really test underwriting standards across the ILS sector. The graph below from Lane Financial LLC on the historical pricing of US military insurer USAA’s senior catastrophe bonds again illustrates how the market has taken on more risk for less risk adjusted premium (exposures include retired military personnel living in Florida).

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The events in the coming days may tell us, to paraphrase Mr Buffet, who has been swimming naked or as Lex put it on Friday, “this weekend may be a moment when the search for uncorrelated returns bumps hard into acts of God”.

Hopefully, all parts of the catastrophe insurance sector will prove their worth by speedily indemnifying peoples’ material losses (nothing can indemnify the loss of life). After all, that’s its function and economic utility to society. Longer term, recent events may also lead to more debate and real action been taken to ensure that the insurance sector, in all its guises, can have an increased economic function and relevance in an increasingly uncertain world, in insuring perils such as floods for example (and avoiding the ridiculous political interference in risk transfer markets that has made the financial impact of flooding from Hurricane Harvey in Texas so severe).

Notwithstanding the insurance sector, our thoughts must be with the people who will suffer from nature’s recent wrath and our prayers are with all of those negatively affected now and in the future.

ILS illuminations

Insurance linked securities (ILS) are now well established in the insurance industry. ILS as an asset class offer, according to its many fans, the benefits of diversification and low correlation to other asset classes whilst offering a stable and attractive risk/reward return. The impact of the new capital on the traditional market has been profound and wide ranging (and a much posted upon topic in this blog – here, here & here for example).

ILS fund managers maintained an “aggressive posture” on price at the recent April renewals according to Willis Re as ILS capacity continues to demonstrate its cost of capital advantage. ILS fund managers are also looking to diversify, moving beyond pure short tail risks and looking at new previously uninsured or underinsured exposures, as well as looking to move their capital along the value-chain by sourcing primary risk more directly and in bulk.

An industry stalwart, John Kavanagh of Willis Re, commented that “with results on many diversifying non-catastrophe classes now marginal, there is greater pressure on reinsurers to address the pricing in these classes” and that “many reinsurers remain prepared to let their top line revenue growth stall and are opting to return excess capital to their shareholders”. The softening reinsurance market cycle is now in its fifth year and S&P estimates that “even assuming continued favourable prior-year reserve releases and benign natural catastrophe losses, we anticipate that reinsurers will barely cover their cost of capital over the next two years”.

Rather than fight the new capital on price, some traditional (re)insurers are, according to Brandan Holmes of Moody’s, “deploying third-party capital in their own capital structures in an effort to lower their blended cost of capital” and are deriving, according to Aon Benfield, “significant benefits from their ability to leverage alternative capital”. One can only fight cheap capital for so long, at some stage you just arbitrage against it (sound familiar!).

A.M. Best recently stated that “more collateralised reinsurance programs covering nonpeak exposures are ceded to the capital markets”. The precipitous growth in the private transacted collateralised reinsurance subsector can be seen in the graph from Aon below.

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Nick Frankland of Guy Carpenter commented that “the capital landscape is ever-changing” and that “such capital diversity also elevates the position of the broker”. Some argue that the all-powerful role of the dominant brokers is exacerbating market softness. These brokers would counterargue that they are simply fulfilling their role in an efficient market, matching buyers and sellers. As Frankland puts it, brokers are “in the strongest position to provide access to all forms of capital and so secure the more beneficial rates and terms and conditions”. Dominic Christian of Aon Benfield commented last year that “to some extent alternative sources of capital are already, and have already uberized insurance and reinsurance, by bringing increased sources of supply”.

Perhaps alone amongst industry participants, Weston Hicks of Alleghany, has questioned the golden goose of cheap ILS capital stating that “some new business models that separate the underwriting decision from the capital provider/risk bearer are, in our view, problematic because of a misalignment of incentives”. Such concerns are batted aside as old fashioned in this new world of endless possibilities. Frighteningly, John Seo of ILS fund manager Fermat Capital, suggests that “for every dollar of money that you see in the market right now, I think there is roughly 10 dollars on the sidelines waiting to come in if the market hardens”.

As an indicator of current ILS pricing, the historical market spread over expected losses in the public CAT bond market can be seen in the exhibit below with data sourced from Lane Financial. It is interesting to note that the average expected loss is increasing indicating CAT bonds are moving down the risk towers towards more working layer coverages.

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In a previous post, I argued that returns from an ILS fund index, with the net returns judgementally adjusted to get to comparable figures gross of management fees, were diverging against those from a pure CAT bond index. I argued that this divergence may illustrate that the ILS funds with exposure to the private collateralised reinsurance sector may be taking on higher risk exposures to pump returns (or may be passing risks amongst themselves in an embryonic spiral) and that ILS investors should be careful they understand the detail behind the risk profiles of the ILS funds they invest in.

Well, the final 2016 figures, as per the graph below, show that the returns in my analysis have in fact converged rather than diverged. On the face of it, this rubbishes my argument and I have to take that criticism on. Stubbornly, I could counter-argue that the ILS data used in the comparison may not reflect the returns of ILS funds with large exposure to collateralised reinsurance deals. Absent actual catastrophic events testing the range of current fund models, better data sources are needed to argue the point further.

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In their annual review of 2016, Lane Financial have an interesting piece on reducing transparency across both the public and private ILS sector. They characterise the private collateralised reinsurance sector as akin to a dark pool compared to the public CAT bond market which they likened to a lit exchange. Decreased transparency across the ILS sector “should send up warning flags” for all market participants as it makes calculating Net Asset Valuations (NAV) with monthly or quarterly frequency more difficult. They argue that the increased use of a relatively smaller public CAT bond market for pricing points across the ILS sector, the less credible is the overall valuation. This is another way of expressing my concern that the collateralised reinsurance market could be destabilising as it is hidden (and unregulated).

In the past, as per this post, I have questioned how the fully funded ILS market can claim to have a lower cost of capital against rated reinsurers who only have to hold capital against a percentage of their exposed limit, akin to fractional banking (see this post for more on that topic). The response is always down to the uncorrelated nature of ILS to other asset classes and therefore its attraction to investors such as pension funds who can apply a low cost of capital to the investment due to its uncorrelated and diversifying portfolio benefits. Market sponsors of ILS often use graphs such as the one below from the latest Swiss Re report to extoll the benefits of the asset class.

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A similar exhibit, this time from a Lombard Odier brochure, from 2016 shows ILS in an even more favourable light!

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As anybody who has looked through any fund marketing metrics knows, performance comparisons with other investment strategies are fraught with bias and generally postdictive. The period over which the comparison is made and the exact indices chosen (look at the differing equity indices used in the comparisons above) can make material differences. Also, the size and liquidity of a market is important, a point which may negate any reliance on ILS returns prior to 2007 for example.

I thought an interesting exercise would be to compare actual historical ILS returns, as represented by the Swiss Re Global Cat Bond Total Return Index, against total returns (i.e. share price annual change plus dividends paid in year) from equity investment in reinsurers across different time periods. The most applicable business model for comparison would be pure property catastrophe reinsurers but unfortunately there are not many of them left.

I have chosen RenRe (RNR) and Validus (VR), from 2007, as representatives of the pure property cat business model, although both have diversified their portfolios away from pure short tail business in recent years. I also selected three of the biggest European reinsurers – Munch Re, Swiss Re and Hannover Re – all of which are large diverse composite reinsurers. Finally, I constructed a US$ portfolio using equal shares of each of the five firms mentioned above (RenRe represents 40% of the portfolio until 2007 when Validus went public) with the € and CHF shares converted at each year end into dollars.

The construction of any such portfolio is postdictive and likely suffers from multiple biases. Selecting successful firms like RenRe and Validus could validly be criticised under survival bias. To counter such criticism, I would point out that the inclusion of the European reinsurers is a considerable historic drag on returns given their diverse composite footprint (and associated correlation to the market) and the exclusion of any specialist CAT firm that has been bought out in recent years, generally at a good premium, also drags down returns.

The comparison over the past 15 years, see graph below, shows that Munich and Swiss struggle to get over their losses from 2002 and 2003 and during the financial crisis. Hannover is the clear winner amongst the Europeans. The strong performance of Hannover, RenRe and Validus mean that the US$ portfolio matches the CAT bond performance after the first 10 years, albeit on a more volatile basis, before moving ahead on a cumulative basis in the last 5 years. The 15-year cumulative return is 217% for the CAT bond index and 377% return for the US$ equity portfolio.

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The comparison over the past 10 years, see graph below, is intriguing. Except for Validus, the CAT bond index beats all other firms and the US$ portfolio for non-volatile returns hands down in the first 5 years. Hannover, Validus and the portfolio each make a strong comeback in the most recent 5 years. The 10-year cumulative return is 125% for the CAT bond index and 189% return for the US$ equity portfolio.

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The comparison over the past 5 years, see graph below, shows that all firms and the portfolio handily beats the CAT bond index. Due to an absence in large loss activity over the recent past and much more shareholder friendly actions by all reinsurers, the equity returns have been steady and non-volatile. The 5-year cumulative return is 46% for the CAT bond index and 122% return for the US$ equity portfolio.

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Overall then, ILS may offer less volatile and uncorrelated returns but I would personally prefer the, often lumpier, historical equity returns from a selected portfolio of top reinsurers in my pension pot (we all could have both in our pension funds!). Then again, the influx of new capital into ILS has put the future viability of the traditional reinsurance business models into question so future equity returns from the sector may not be too rosy.

At the end of the day, the bottom line is whether current market risk premia is adequate, irrespective of being supplied by ILS fund managers or traditional reinsurers. Based upon what I see, I have grave misgivings about current market pricing and therefore have no financial exposure, ILS or equity or otherwise, to the market at present.

Additional Comment, 29th April 2017: The ILS website Artemis.bm had an interesting piece on comments from Torsten Jeworrek of Munich Re during their March conference call. The applicable comment is as follows:

“And now I give you another example, which is not innovation per se or not digitalization, but you know that more and more alternative capital came into the insurance industry over the last years; hedge funds, pension refunds, participating particularly in the cat business and as a trend that not all of the limits they provide, cat limits are fully collateralized anymore. That means there are 10 scenarios; hurricane, earthquake, [indiscernible], and so on; which are put together, but not 10 times the limit is collateralized, let’s say only 4 times, 5 times.

That means these hedge funds and pension funds so to speak in the future if they don’t have to provide full 100% collateralized for all the limits they provide, they need a certain credit risk for the buyer. The more they entertain, the more there’s a likelihood that this reinsurance can also fail. The question is how far will that go and this kind of not fully collateralized reinsurance, will that be then accepted as a reinsurance by the regulator or will that be penalized at a certain time otherwise we don’t have level playing field anymore, which means the traditional reinsurer who was strongly monitored and regulated and also reported as really expensive and a burden for our industry and for us and on the other hand, you have very lean pension and hedge funds who even don’t have to provide the same amount of capital for the same risk.”

Divine Diversification

There have been some interesting developments in the US insurance sector on the issue of systemically important financial institutions (SIFIs). Metlife announced plans to separate some of their US life retail units to avoid the designation whilst shareholder pressure is mounting on AIG to do the same. These events are symptoms of global regulations designed to address the “too big to fail” issue through higher capital requirements. It is interesting however that these regulations are having an impact in the insurance sector rather than the more impactful issue within the banking sector (this may have to do with the situation where the larger banks will retain their SIFI status unless the splits are significant).

The developments also fly in the face of the risk management argument articulated by the insurance industry that diversification is the answer to the ills of failure. This is the case AIG are arguing to counter calls for a breakup. Indeed, the industry uses the diversification of risk in their defences against the sector being deemed of systemic import, as the exhibit below from a report on systemic risk in insurance from an industry group, the Geneva Association, in 2010 illustrates. Although the point is often laboured by the insurance sector (there still remains important correlations between each of the risk types), the graph does make a valid point.

click to enlargeEconomic Capital Breakdown for European Banks and Insurers

The 1st of January this year marked the introduction of the new Solvency II regulatory regime for insurers in Europe, some 15 years after work begun on the new regime. The new risk based solvency regime allows insurers to use their own internal models to calculate their required capital and to direct their risk management framework. A flurry of internal model approvals by EU regulators were announced in the run-up to the new year, although the amount of approvals was far short of that anticipated in the years running up to January 2016. There will no doubt be some messy teething issues as the new regime is introduced. In a recent post, I highlighted the hoped for increased disclosures from European insurers on their risk profiles which will result from Solvency II. It is interesting that Fitch came out his week and stated that “Solvency II metrics are not comparable between insurers due to their different calculation approaches and will therefore not be a direct driver of ratings” citing issues such as the application of transitional measures and different regulator approaches to internal model approvals.

I have written many times on the dangers of overtly generous diversification benefits (here, here, here, and here are just a few!) and this post continues that theme. A number of the large European insurers have already published details of their internal model calculations in annual reports, investor and analyst presentations. The graphic below shows the results from 3 large insurers and 3 large reinsurers which again illustrate the point on diversification between risk types.

click to enlargeInternal Model Breakdown for European Insurers and Reinsurers

The reinsurers show, as one would expect, the largest diversification benefit between risk types (remember there is also significant diversification benefits assumed within risk types, more on that later) ranging from 35% to 40%. The insurers, depending upon business mix, only show between 20% and 30% diversification across risk types. The impact of tax offsets is also interesting with one reinsurer claiming a further 17% benefit! A caveat on these figures is needed, as Fitch points out; as different firms use differing terminology and methodology (credit risk is a good example of significant differences). I compared the diversification benefits assumed by these firms against what the figure would be using the standard formula correlation matrix and the correlations assuming total independence between the risk types (e.g. square root of the sum of squares), as below.

click to enlargeDiversification Levels within European Insurers and Reinsurers

What can be seen clearly is that many of these firms, using their own internal models, are assuming diversification benefits roughly equal to that between those in the standard formula and those if the risk types were totally independent. I also included the diversification levels if 10% and 25% correlations were added to the correlation matrix in the standard formula. A valid question for these firms by investors is whether they are being overgenerous on their assumed diversification. The closer to total independence they are, the more sceptical I would be!

Assumed diversification within each risk type can also be material. Although I can understand arguments on underwriting risk types given different portfolio mixes, it is hard to understand the levels assumed within market risk, as the graph below on the disclosed figures from two firms show. Its hard for individual firms to argue they have material differing expectations of the interaction between interest rates, spreads, property, FX or equities!

click to enlargeDiversification Levels within Market Risk

Diversification within the life underwriting risk module can also be significant (e.g. 40% to 50%) particularly where firms write significant mortality and longevity type exposures. Within the non-life underwriting risk module, diversification between the premium, reserving and catastrophe risks also add-up. The correlations in the standard formula on diversification between business classes vary between 25% and 50%.

By way of a thought experiment, I constructed a non-life portfolio made up of five business classes (X1 to X5) with varying risk profiles (each class set with a return on equity expectation of between 10% and 12% at a capital level of 1 in 500 or 99.8% confidence level for each), as the graph below shows. Although many aggregate profiles may reflect ROEs of 10% to 12%, in my view, business classes in the current market are likely to have a more skewed profile around that range.

click to enlargeSample Insurance Portfolio Profile

I then aggregated the business classes at varying correlations (simple point correlations in the random variable generator before the imposition of the differing distributions) and added a net expense load of 5% across the portfolio (bringing the expected combined ratio from 90% to 95% for the portfolio). The different resulting portfolio ROEs for the different correlation levels shows the impact of each assumption, as below.

click to enlargePortfolio Risk Profile various correlations

The experiment shows that a reasonably diverse portfolio that can be expected to produce a risk adjusted ROE of between 14% and 12% (again at a 1 in 500 level)with correlations assumed at between 25% and 50% amongst the underlying business classes. If however, the correlations are between 75% and 100% then the same portfolio is only producing risk adjusted ROEs of between 10% and 4%.

As correlations tend to increase dramatically in stress situations, it highlights the dangers of overtly generous diversification assumptions and for me it illustrates the need to be wary of firms that claim divine diversification.

Obstinate OpRisk

Insurers and banks are currently grappling with how best to model operational risk. Many firms struggle to come up with sensible figures that can past any proper validation criteria when faced with issues like limited applicable data, correlation with other risks, aggregation challenges, and the impact of prospective operational risks from the use of new technology.

McKinsey have an interesting article out suggesting a structured approach to the issue. The graphic below illustrates their approach.

click to enlargeMcKinsey Operational Risk Exhibit

No one approach is ideal or applicable to all. Notwithstanding this, financial firms all too often focus on justifying a low operational charge to regulators in their capital modelling rather than ensuring that their approach can be embedded into their control framework as a practical risk management tool for management and employees to use in being continually vigilant of operational risks whilst offering tangible incentives to mitigate risk which are, by their nature, unanticipated.