Economic Capital Modelling O 201208

FINANCIAL SERVICES

Economic Capital Modeling in the

Insurance Industry A solid foundation

for future advantage?

August 2012

kpmg.com

http://www.kpmg.com

2012 KPMG International Cooperative (KPMG International), a Swiss entity. Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. All rights reserved.

ContentsAbout the survey 3

Introduction 4

Unlocking the value of Economic Capital 6

Growing pains of implementation 12

New trends emerging 16

Allowing for sovereign default risk 17

Enhancements of the future 18


2 | Economic capital


About the survey*

KPMGs Global Economic Capital (EC) Survey questioned 43 of the largest global insurers between October 2011 and March

2012, covering four major territories: Europe (35 percent), North America (32percent), Asia (19 percent) and South Africa (14 percent).

Responses were received from all types of insurers, including life insurers (47 percent), general insurers (14 percent), reinsurers (9 percent)

and composite insurers (30percent). Over 90percent of responses came from the head of EC, chief risk officer, chief actuary, head of Solvency II or

other senior director.

The survey asked detailed questions on emerging EC practices. Specifically, it covered the EC framework; modeling techniques; lite modeling approaches,

such as curve fitting and replicating portfolios; dependency, aggregation and allocation approaches; projection techniques; and management understanding.

* Please note, for ease of reference, figures have been rounded up or down to the nearest whole percent, so not all graphs, charts and statistics add up to precisely 100 percent.

Economic capital | 3


Introduction

Economic capital (EC) modeling has the potential to transform the way firms control risks and manage their businesses right across the global insurance industry, providing a firm foundation for success. A complex way of calculating how much capital a business needs to meet its future risks, EC modeling has its roots in the banking sector in the late 1970s. It is only over the past 10 years, however, that insurance companies truly started to integrate EC frameworks into their operations.

EC implementation is complex. One-third of respondents to our survey have spent over US$30 million on implementation costs, some have spent in excess of US$75million. At the same time, companies commit to significant recurrent costs to maintain EC after implementation.

A fully-integrated EC model provides companies with tools to better understand risk and, potentially, the ability to price it more accurately. If used effectively, EC modeling allows management to identify and quantify the risk exposure of a firm explicitly, rather than providing for risks with margins and capital requirements that do not vary according to the risk profile. By improving the transparency of an insurers risk profile, EC can help management deliver value for shareholders while ensuring adequate protection of policyholder benefits.

Key findings from our survey

Our survey shows that EC is now used widely across the insurance industry as a key tool for the financial management of the business. Today, most respondents use EC to manage and monitor risks, but some companies do not yet use EC for strategic and business decisions, such as pricing and shareholder value management. There appears to be a lack of understanding at the very top of insurance organisations that could be hindering the effectiveness of the techniques. 40 percent of respondents indicated that management understanding of EC is limited, suggesting more training on EC is still required. This is particularly important in an environment where:

ECwillbeusedbyregulatorstomonitorsolvency(SolvencyIIinthe European Union);

CompaniesarebeginningtocommunicateECandeconomicsolvencytoinvestors; and

ECisanintegralpartofanyOwnRiskandSolvencyAssessment(intheEUunderSolvency II, but also in the United States where the current expectation is that it will be a requirement from 2014).



As insurance company managers now begin to use EC as an input into business decisions, companies need up-to-date reporting and are looking to use new techniques to deliver results more quickly. Curve fitting emerges as the preferred new approach to update results with over half of companies adopting this approach or variant in some part of their EC calculation. Key areas for improvement include a better understanding of dependency between risks, modeling how capital can be moved around a group and implementing new techniques to project future economic capital requirements. These improvements are required for a robust analysis of how economic capital can change over time and as management decisions are taken.

Despite significant investment, our survey shows that companies have embarked on a journey of continuous improvement. This includes technical aspects and methodology, but also education and communication to support wider embedding across the firm. At the same time, firms need to be agile as new risks emerge and unexpected issues arise in the future. For example, some two-thirds of respondents do not allow for risks on sovereign debt. These investments were considered risk free prior to the 2008 crisis, although it is hard to see how any investment can be deemed risk free in current turbulent times. This example highlights the need to challenge assumptions and inputs on a continuous basis as the external environment develops.

But as the first wave of EC adopters settle into their new modeling frameworks, our survey suggests there is a danger that a short-term focus on regulatory compliance is masking a lack of understanding of these complex models. As a result, insurers are missing the opportunity to build a better understanding of their risk and capital profile and strengthen the foundations of their business.

Just as a commercial architect knows that for his buildings to stand the test of time, he needs to use specialist engineering techniques and construction teams to build the strongest possible foundations. Similarly, senior management of insurance companies need to fully understand how EC modeling techniques their scope, strengths and weaknesses can be harnessed to provide a more risk aware, better-protected and sustainable basis for their businesses over the long-term.

If properly understood and implemented, EC frameworks provide not only a roadmap for compliance, they may provide invaluable information for risk appetite, risk management, capital allocation, pricing, underwriting and strategic decisions. But if used ineffectively, they can become expensive tick-box exercises that hamper long-term decision making. Moreover, they can mislead management into falsely believing risks are adequately covered or force them into actions that go against sound business principles.



Internal and externalreporting

7%

66%

27%

0%

20%

40%

60%

80%

100%

Current Planned Dont know

Capital allocation and risk appetite

64%

36%

3%

Strategicdecisions

71%

26%

Risk managementand mitigation

79%

21%

Pricing/underwritingdecisions

55%

35%

10%



Unlocking the value of Economic CapitalEC has moved beyond the computations and the calculations and is well on its way to becoming an integral part of the way companies manage their business on a day-to-day basis.

Our survey indicates almost all the top global insurers are already using EC methods to support their risk management systems, with 100 percent of those surveyed in Europe, North America and South Africa adopting it to some extent. In Asia, there are significant differences in the stages of development between the international groups operating in the region, regional groups that operate across Asia and domestic companies focused on a particular market. That said, 63percent of companies operating in the region have implemented an EC framework, with the remaining 37 percent intending to introduce a framework within the next three years.

While most respondents are using EC for risk management, many have not yet realized the potential advantage of using it to support wider business decisions (Figure 1).

Figure 1: Application of EC

Source: KPMG International Economic Capital Survey, 201112

0 1 2 3 4 5

Improved risk awareness 4.56

Competitive reasons 3.14

Other 4.00

3.85Rating agency and regulatory compliance

4.42Better risk-return decision making

3.58Improved shareholder value

All participants

1 = not at all important, 5 = very important



The lower application of EC in this area is linked to the lower perceived benefits in respect of improving shareholder value and enhancing competitive position (Figure 2).

Figure 2: Benefits of adopting an EC


So, what factors inhibit companies from unlocking these benefits? Twopossible reasons may be a lack of understanding at the top of the organization of the underlying EC methods and the lack of timely information to facilitate effective decision making.

Projectiontechniques

2.6

1.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

EC approach

2.8

3.4

Risk functionmodeling

2.9

2.3

Lite models/lossdistribution

2.8

1.1

Dependencystructure

2.5

1.9

Refinementtechniques

2.3

1.3

Allocationmethodology

2.7

2.2

Europe Other 1 = no understanding, 5 = full understanding



Lack of management understandingWorryingly, 40 percent of responses indicate that management understanding of the EC framework is limited (i.e. rated less than or equal to two, with one being no understanding and five beingfully understood). Generally, European companies ranked management understanding higher than other territories (Figure 3), which is not surprising given that Solvency II requires firms to explicitly demonstrate this as part of their Own Risk and Solvency Assessment (ORSA) and internal model approval requirements. However, even in Europe, it is clear that management understanding needsimprovement.

Figure 3: Management understanding


There are reasons why current management understanding is not high. The techniques are relatively new compared to other metrics that have traditionally been the cornerstone of insurance financial management. Furthermore, the techniques are constantly becoming more sophisticated to meet desired statistical requirements.

Clearly, the current state cannot continue. Companies will need to address the knowledge gap and ensure that management stay abreast of the latest developments. The price of potential failure is high the loss of competitive advantage by not using techniques to drive value or worse, placing too much reliance on model results without fully considering the boundaries within which they should be interpreted.

Effective board-level training is also required, including education sessions from experts, scenario type workshops and continuous training programs. Adequate attention should also be given to improving the quality of management information and providing sufficient granular analysis to empower management to make informed decisions.

Lack of timely informationTo use EC information effectively, it must be up-to-date and reported on a timely basis. The rapid changes in financial markets since the 2008 financial crisis and the associated volatility they brought to insurers balance sheets have clearly highlighted that previous standards of annual or ad hoc reporting will no longer be acceptable for company management.

In Europe, quarterly reporting has now emerged as the industry standard, with 60 percent of companies reporting this frequently. In North America, no respondents indicated they were reporting more frequently than quarterly and more than half said they report only once or twice a year. InAsia, 63percent said they were reporting quarterly and, in South Africa, 50percent are reporting quarterly.

Clearly, the industry has more work to do in this area. An unfavorable comparison could be made to the hedge fund industry, where it is the norm to have daily reporting on positions. On any given day, the finance director of a hedge fund will know precisely what the risk sensitivities are and what would happen if key risk factors moved in a particular way.

While reporting daily is not yet realistic for insurers, senior management needs to make a judgment call on what level of frequency is required to effectively capitalize on the benefits of an economic framework. The more infrequent and out-of-date the reporting, the less likely it will be seen as a credible tool that can add value to thebusiness.

Companies that embrace an EC framework and use it throughout their business will undoubtedly have a competitive advantage in the long term. They will ensure appropriate returns are achieved for risks they take on, and will be less likely to take on risks that they dont fully understand. Moreover, through the full integration of an EC framework into the business, they will have a better understanding of the limits of EC modeling and when not to rely on it.



Growing painsof implementation

As with any rapid change, there are challenges in implementation. It is no different for the adoption of ECwithin the insurance industry. According to our survey, the two main issues related to the implementation of EC frameworks are data quality and availability, and the complexity of the calculations required. These two issues were the most highly-rated across Europe, Asia, North America and South Africa. Other challenges, particularly outside of Europe, included winning executive buy-in and attracting the appropriate expertise required to build EC tools. Last, but not least, was implementation cost.

Figure 4: Challenges with respect to EC framework in different regions

0.0 1.0 2.0 3.0 4.0 5.0

Data quality andavailability

Complexity andcomputation

Production requirements(e.g. model run timesand number of runs)

IT infrastructure

In-house expertise

Executive buy-in

Asia South Africa Continental Europe UK All ParticipantsNorth America 1 = not challenging, 5 = challenging





Weaknesses in data quality and availabilityThe challenge of data quality is probably linked to the fact that, historically, insurance companies have not been strong in the area of data management. A history of mergers and takeovers has left much of the industry burdened with out-of-date and patched-up legacy IT systems. Extracting quality data from such systems and being able to report it in a useful way for EC modeling constitutes a significant challenge. In Europe, companies have been forced to address this problem as part of their Solvency II compliance, largely through significant investment in data warehouses and other IT infrastructure.

Another factor is the unavailability of data. EC modeling requires a significant amount of historic data to understand how portfolios have performed in the past. For example, to understand the risks in equity markets, firms need to understand what has happened in equity markets over the past 100 years. This is a challenging but achievable task, given the availability of data dating from the early 20th century until now. But when it comes to companies own internal data, such as the behavior of their portfolios or their customers over a similar length of time, the data often does not exist, which can compromise the inputs to the capital models.

The lack of data can lead to a heavy reliance on expert judgment to fill the gap. But expert judgment is by definition subjective and can materially affect results. This can cause some concern about the objectiveness of the model and how much management can truly rely onitsresults.

Complexity of computationsEC calculations are complex and can take time to produce accurately. Many companies struggle with implementing EC modeling frameworks that produce accurate information quickly enough to allow them to respond in a meaningful way.

Companies are responding to these challenges by introducing simplifications and approximations into their models. While these are an inevitable part of implementing the model, it is vital that companies understand the limitations of these adjustments and set appropriate tolerance limits around their application.


Winning executive buy-inOutside Europe and particularly in Asia,

winning executive buy-in is seen as a significant challenge. This is not surprising due

to the relative lack of regulatory pressure in other regions, meaning there is no strong regulatory

driver pushing management to engage with EC modeling. In addition, if the result of full EC

implementation is expected to lead to higher capital requirements, then management may be reluctant

to adopt an EC approach. In these circumstances, it will be harder for companies outside Europe to achieve

buy-in for the investment required to embed an effective framework across an organization.

Resource constraintsAnother challenge highlighted by the survey is the availability

of expertise. In Europe, companies have tended to bring in large numbers of external consultants and contractors to

develop their EC frameworks to meet Solvency II. With those frameworks now in place, many are trying to scale back that

resource, running the risk that those people with the most knowledge of EC techniques will leave the business.

Resources to implement EC will be at a premium for the next several years and will put pressure on the actuarial and financial resources of

major insurance groups. Groups need to be prepared to pay enough to recruit the right talent. They must also be willing to structure their

organizations in ways that ensure clear ownership and responsibilities around EC modeling, perhaps as part of a wider review of their operating

models. In Europe, insurers need to look beyond the traditional actuarial and risk roles to areas such as banking and more quantitative backgrounds

in order to identify the right skills. In Asia, meanwhile, companies can recruit from similar fields while also leveraging knowledge from Europe and North

American markets which have already implemented EC modeling and can bring their practical experience to bear.



30%

22%19%

14%

8%

8%

Less than 1 1-5 5-10 10-20 20-50 50 and above

45%

32%

13%

8%3%

0%

Less than 1 1-5 5-10 10-20 20-50 50 and above

High setup and maintenance costs



Figure 5: Distribution of cost of establishing EC framework all insurers (in EUR million)


Figure 6: Distribution of cost of maintenance of EC framework all insurers (in EUR million)


Setup costs were unsurprisingly high in Europe, withmore than 60 percent of companies that provided a response indicating spends in excess of US$75 million. The costsin other jurisdictions were lower, reflecting the lower regulatory incentive to invest in these techniques(Figure 5).

Ongoing maintenance costs for North America and Europe are between 2 percent and 10 percent per annum of the original setup costs. Expected maintenance costs for otherterritories are between 20percent and 80 percent of setup costs (Figure6).

So, was the extra spend in Europe as a result of Solvency II necessary to achieve outcomes in respect of EC? Some market experts believe SolvencyII is an over-engineered response to the issue it was designed to address. If this is true, there has undoubtedly been additional cost to the industry than was required. Alternatively, it could be argued that Solvency II triggered a process for European insurers to address shortcomings in risk technology and data infrastructure. Perhaps in some way, this level of investment would be required at somepoint in the future. One thing is certain a lackofregulatory guidance and shifting requirementshave caused additional spend for European companies, as they continually adapt to the changing landscape. Hopefully, regulators outside of Europe will learn from the lessons of Solvency II in their own development of risk-based regimes.

Stress-based Simulation-based Combination of stress and simulation

49%

30%

21%

Curve fittingReplicating portfoliosLeast Squares Monte Carlo (LSMC)

13%

29%58%



New trendsemerging

Even though EC modeling is relatively new and subject to constantly changing requirements, our survey highlighted some new trends that are emerging. These included the move to sophisticated simulation-based approaches and the wide take-up of curve fitting techniques (Figures 7 and 8).

Figure 7: Distribution of techniques used for EC computation


Figure 8: Distribution of lite modeling techniques


The decline of the stress approachIn Europe, regulators expect companies to demonstrate their understanding of the full distribution of their risks and this can only be achieved through a simulation-based approach. This is a more theoretically correct approach to calculating EC, as it allows for all the interactions of each risk over many possible outcomes or simulations. A stress-based model, in contrast, only shows what losses would be at a particular point; it does not show the behavior of the tail of the loss distribution, where the real risk may lie.

The survey results show that industry practice appears to be moving toward a simulation-based approach to EC computation. In the UK, 71percent of respondents said they used asimulation-based approach, while 71 percent in the rest of Europe used either simulation or a combination of simulation and stress-based approaches, as do 86 percent in North America and 88 percent inAsia.

This increased sophistication, however, comes at a price a lack of transparency of the key capital drivers, unless supported by adequate management information, training, documentation and a deep understanding of the business from senior management.

The rise of curve fittingIn moving toward a simulation-based

approach, insurers are favoring lite modeling techniques as opposed to full models. Alite

model is a simplified representation ofthe full actuarial projection or asset model

that can be used as a substitute in some circumstances. Examples of lite models

include replicating portfolios and curve-fitting techniques.

Two or three years ago, there was no consensus about the preferred lite modeling

technique. Early adopters implemented replicating portfolio approaches, but over

the past 12 months curve fitting (and its variants) has emerged as the preferred

approach. The increased take-up of curve fitting is probably driven by its ability to be

applied to the entire risk taxonomy (both market and non-market risks), as opposed

to a replicating portfolio, which typically focuses on only market risk.

65%

35%

Yes No



Allowing for sovereigndefault risk

Historically, companies have not felt the need to worry about sovereign default risk. Governments were assumed to be virtually risk-free, but recent events in Europe and North America have turned that assumption on its head.

Our survey showed that 65 percent of respondents are currently not allowing for sovereign default risk in their EC models (Figure 9). Of those that do allow for some form of sovereign default risk, it is typically European companies (60 percent), a likely reflection of the Eurozone government debt crisis. It is interesting that only 36 percent of North American companies do allow for this risk, given the recent US government ratings downgrade. Even where companies do make some allowance for this risk, the level of sophistication of the techniques used is limited.

Figure 9: Allowance for sovereign debt risk


On the other hand, there is much debate about the issue of whether it is fair to expect companies to hold capital to provide better guarantees to their customers than a sovereign government can provide to its bond holders. The situation in Greece has shown that there are real risks around sovereign bonds. Should insurance companies have to hold capital to cover the potential losses? Isitfair to ask insurance companies to provide more security than a solvent state provider? Although it is Europe that provides the most graphic illustration of the risks involved and where the risks are arguably higher due to the inflexibility of the single currency this is very much a global issue and likely to be the subject of further debate in the industry.

0%

20%

40%

60%

80%

100%

Riskdistributions

Economic lossmodeling

Dependencymodeling

Refinementtechniques

Projectiontechniques

Software

Currently satisfied with Plan to develop in next 12 months No reply

60%

37%

3%

56%

42%

2%

49%

49%

2%

26%

70%

4%

30%

67%

3%

37%

60%

3%



Enhancements ofthe future

EC has undoubtedly moved out of the laboratory and has become part of the way businesses are managed. However, there is still more work to do. 93 percent of respondents indicated they plan to improve at least one of the core components of their EC framework over the next 12 months, more than 60percent indicated they would develop three or more components (Figure 10).

Figure 10: Indications of future development


Correlation and dependency modeling Respondents in all regions were dissatisfied with the correlation and dependency modeling capabilities of their EC framework, with 88 percent of those in Asia not satisfied. It is perhaps a reflection of Asias slower take-up of economic modeling, because these techniques only really come into play once an ECmodel is firmly inplace.

Dissatisfaction with correlation and dependency modeling reflects the lack of hard data to analyze how risks are interlinked and how chains of events can occur together. In the absence of data, the majority of respondents are using expert judgment and simplified techniques such as correlation-based approaches to aggregation.

This level of substantial subjectivity in such a core area of the EC calculation (companies diversification benefits typically range from 30 to 70 percent of pre-diversified capital), needs to be properly considered. European regulators are already indicating this will be an area of focus in their internal model approval process. For example, Julian Adams, Director of Financial Supervisory Authority Insurance Supervision, Prudential Business Unit, in his speech at the City & Financial Conference in April, said firms need to properly consider how risks interact and how their behavior can change the tails of distribution. Companies will need to have in place robust, considered and documented processes if they are to emerge unscathed from regulator reviews.

3.6

Reinsurance

1 = not effective, 5 = very effective

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

3.2

Non-linearity

3.2

Tail dependency

2.7

Fungibilityconstraints

3.3

Managementactions

3.6

Group structure



Refinements Even after the initial calculation of EC, there are a number of adjustments or refinements that may be required to reflect items that may not be adequately captured in the model. Fifty-sevenpercent of UK respondents said they were unhappy with the current techniques used to capture these adjustments, rising to 67 percent across Europe as a whole, 75 percent in Asia and 85 percent of respondents in NorthAmerica.

The survey results indicate that allowing for fungibility constraints with the EC calculations was one of the hardest aspects to incorporate (Figure 11).

Appropriately allowing for fungibility constraints is critical to ensuring that EC is not understated by taking credit for capital movements that would be difficult to achieve in practice. However, it is a major challenge due to the

complex capital structures companies have typically had in place. These include complex group structures, contingent loans, internal reinsurance and other support arrangements.

Such arrangements have normally been developed to improve tax efficiency, or as part of historic mergers & acquisitions activity, but they add to the complexity of EC computation. Advanced companies have started to include these constraints in their models. This is not a trivial task. It may require management to think through restrictions on the transferability of capital in different geographies, in extreme scenarios, and also the priority for distributing surplus when more than one entity is in deficit at thesame time.

Figure 11: Additional EC model considerations


Projection techniquesMuch of the historic development of EC modeling has focused on calculating the opening position, with little focus on projected results. However, to effectively use EC in business, it needs to be incorporated into the business planning and pricing process both of which require a robust projection methodology. In Europe, projected results are also a key part of the Pillar II ORSA requirements, compelling companies to demonstrate an understanding of the development and emergence of risk over the business horizon (typically three years).

The issue that respondents recognize is that they generally do not have sophisticated methodologies in place. 56 percent of respondents

that currently project EC adopt factor-based approaches, with the complexity of the factors varying significantly between companies. Furthermore, there is a lack of understanding about how robust projection methodologies are in stress scenarios or how wide the funnel of doubt becomes as you increase the projection period. The combined effect is that 67 percent of companies indicated they were looking to further develop their techniques over the next 12 months.

Factor-based/risk driver approach Direct evaluation

Other Nested stochastic simulation

5%

56%

13%

26%


Figure 12: Distribution of lite modeling techniques



0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1 = not effective, 5 = very effective

3.93.7

3.5

2.3

Market risk Insurance risk Credit risk Operational risk


Operational risk modelingHistorically, operational risk has not been a big focus for companies. Therefore, it is not surprising that this is seen as the least effective part of a companys framework (Figure 13).

Figure 13: Effectiveness of risk modeling


Increased regulatory focus and a better understanding of how operational failures can affect the business are driving companies to review their approachinthis area. In Europe, there has been a clear move to advanced stochastic-based operational risk models with more than 60 percent of respondents having adopted this type of model.

The difficulty is that there are many areas of subjectivity in the operational risk framework that complicate the modeling. For example, leaked policyholder information may have resulted from a number of possible causes, such as IT failure, human error and fraud, with the exact cause being difficult to pinpoint. Furthermore, the

potential losses as a result may not be known for many years, if at all.

Compounding this is the fact that most companies have not recorded historic operational losses, meaning there is a lack of data available to calibrate the model. To address these issues, companies are establishing operational loss databases and looking to supplement their own limited data sets with external operational loss data provided by consortiums such as the Operational Risk Consortium (ORIC).

It will be many years before the industry has enough credible data to underpin their operational risk models. This fact is reflected in the survey results, which

showed that out of the 14 companies having adopted a stochastic-based model, 13 rely on expert judgment to calibrate it.

While it is natural to expect refinement over time, the current level of activity across the industry is unprecedented in terms of its extent and nature. Furthermore, it is unlikely to be replicated in the near future. Clearly, companies are investing now in building their EC framework to give their businesses the best possible foundations for long-term advantage. The costs of this investment are high in the short term, but as any architect will confirm, investing in solid, appropriate foundations at the outset is the best indicator of future stability.


ContactsIf you have any questions about the survey, about EC in general, or if you would like a copy of the full report, please contact:

Global Insurance Leadership Team

Frank Ellenbrger Global Head of Insurance and EMA Coordinating Partner KPMG in Germany T: +49 89 9282 1867 E: [email protected]

Simon Donowho ASPAC Coordinating Insurance Partner KPMG in China T: +852 2826 7105 E: [email protected]

Laura Hay Americas Coordinating Insurance Partner KPMG in the US T: +1 212 872 3383 E: [email protected]

Frank Pfaffenzeller Global Insurance Audit Lead Partner KPMG in Germany T: +49 89 9282 1027 E: [email protected]

Gary Reader Global Insurance Advisory Lead Partner KPMG in the UK T: +44 20 7694 4040 E: [email protected]

Tim Roff Global Actuarial Lead Partner KPMG in the UK T: +44 20 7311 5001 E: [email protected]

Hugh von Bergen Global Insurance Tax Lead Partner KPMG in the UK T: +44 20 7311 5570 E: [email protected]

Editorial Team

Ferdia Byrne Partner KPMG in the UK T: +44 20 7694 2984 E: [email protected]

David HonourPrincipal Advisor KPMG in the UKT: +44 20 7694 2358E: [email protected]

Neil ParkinsonPartner KPMG in CanadaT: +1 416 777 3906E: [email protected]

David L. WhitePrincipal, Advisory KPMG in the UST: +1 404 222 3006E: [email protected]

Thomas S. McIntyrePrincipal, AdvisoryKPMG in the UST: +1 860 297 5512E: [email protected]

Martin NobleSenior Manager KPMG in Hong KongT: +852 2685 7817E: [email protected]

Douglas LecocqPrincipalKPMG in Hong KongT: +852 2978 8282E: [email protected]

Peter WitheyAssociate Director KPMG in South AfricaT: +27 827 191 654E: [email protected]

The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.

2012 KPMG International Cooperative (KPMG International), a Swiss entity. Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. No member firm has any authority to obligate or bind KPMG International or any other member firm vis--vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm. All rights reserved.

The KPMG name, logo and cutting through complexity are registered trademarks or trademarks of KPMG International.

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Publication name: Economic capital

Publication number: 120821

Publication date: August 2012

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Economic Capital Modelling O 201208

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