Greater than the sum of the parts by Peter Bonisch

Netik White Paper Solvency II applications

Greater than the sum of the parts The new investment Gestalt under Solvency II Building a base for the firm’s ‘analytic core’

in association with Paradigm Risk

Netik White Paper Solvency II applications

Greater than the sum of the parts The new investment Gestalt under Solvency II Building a base for the firm’s ‘analytic core’

in association with Paradigm Risk

Greater than the sum of the parts > CONTENTS

4 7 8 10 12 15 17 19

Executive summary Introduction How different from ICAS? A framework for Solvency II Focus on measuring investment risk Case Study: Netik InterView Insurance risk management – the bigger picture Conclusion

Endnotes About Netik

Drafted by Paradigm Risk as a joint initiative between Paradigm Risk and Netik LLC to improve understanding of investment and data-related issues in Solvency II © 2011 Paradigm Risk Limited. All rights reserved © 2011 Netik LLC. All rights reserved.

Solvency II governance, investment & data implementation issues

With Solvency II we are preparing the most significant regulatory changes in the insurance sector in 30 years. Michel Barnier, EU Commissioner Internal Markets & Services, May 2010

Solvency II is not just about capital. It is a change of behaviour. Thomas Steffen, Chief Executive Director of Insurance Supervision, BaFin Chairman of CEIOPS, July 2007

All models are wrong, but some are useful . . . The practical question is how wrong do they have to be to not be useful. George Box, Professor of Statistics, University of Wisconsinâ&#x20AC;&#x201C;Madison, 1987

Greater than the sum of the parts

Executive summary Solvency II embraces a market-consistent approach to valuation of assets and liabilities of insurance firms. By the implementation date of 1 January 2013 (or internal model operation date, if relevant), the firms pursuing internal models will need to have a well-rehearsed and operating methodology for doing so, both according to the formulae prescribed in EIOPA’s (formerly CEIOPS’) guidance and, for all firms including those using the prescribed standard models, following their own assumptions and preferences in the own risk & solvency assessment or ORSA.

Firms need to ensure they can follow markets closely through, in some cases, greatly increased market risk infrastructures . . . Access to current and historic market data and effective data warehousing will be essential for all firms pursuing internal models.

Insurance firms will, by and large, have the knowledge resources and capacity in place to deal with the changes to their technical provisions – to the valuation approaches to liabilities – through their existing internal or external actuarial resources. Insurers’ core expertise is in management of the risk profile of liabilities; matching has been important, but management of risks associated with assets has been secondary or effectively ‘contracted out’ to asset managers. Despite the preparation of the FSA’s individual capital adequacy standards (ICAS) regime, the more challenging area technically will be in the other major risk classes: market risk, counterparty or default risk and operational risk. Firms need to ensure they can follow markets closely through, in some cases, greatly increased market risk infrastructures – data, technology and third-party risk applications and that they can form an integrated assessment of their capital needs through optimising their asset positions and allocations relative to their books of underwritten risks. Access to current and historic market data and effective data warehousing will be essential for all firms pursuing internal models. In larger and more complex firms, supervisors will expect the ability to assess capital implications across both sides of the balance sheet for all strategic and major tactical decisions by the firm – the ‘use test’. This will require far greater capacity to undertake integrated stochastic modelling necessitating a significant increase in computation capacity or implementa-

tion of replicating portfolios of assets to ease computation load – shifting the load to mimicking the liability portfolio through traded financial instruments and options structures. Perhaps most challenging of all, firms will need to grapple with the implications of the profound challenges that have been posed to VaR by the catastrophic failures of risk management during the build-up to the recent global financial crisis. While the mathematics of VaR are unassailable, many of the distributional assumptions used in VaR calculations have been found desperately wanting. The behavioural implications of defaulting to the standard regulatory positions for VaR are material; far more relevant to risk managers than the risks reflected in VaR are those not reflected in VaR – the ‘fat-tail’ risks and extreme risks of uncorrelated risks suddenly becoming highly correlated (‘decaying to 1’) under stressed market conditions. For these risks, other risk approaches will be needed. These will centre around use of plausible economic scenarios for stress testing, including for ‘reverse stress testing’ – pushing the scenario assumptions to failure conditions and developing thresholds and coping strategies for firm-level response to crisis. Without exceptions, firms deploying internal models for market risk will need to undertake extensive back-testing of their risk assumptions and VaR inputs. Here, access to current market data for robust valuation and comparable and reliable time series of price data for relevant instruments (for mark-to-market and mark-to-model) will be essential; reliable storage, warehousing architectures, access routines, intuitiveness of user interface and ease and flexibility of reporting will all be critical. Choosing the right data warehouse and aggregation engine will be one of the most critical choices during the Solvency II implementation. There is no doubt that supervisory standards for data quality will be higher for insurers under Solvency II. Solvency II establishes the need for the firm to create an analytic core

Solvency II governance, investment & data implementation issues

– a clear and demonstrable linkage between its data, modelling and capital requirements. The regulation is built around a clear objectivist logic that requires that the firm demonstrate that it pulls the results from the firm’s analytical core through to its decision-making. This places data coverage and quality front and centre in the firm’s Solvency II initiatives and at the heart of its on-going governance. Solvency II represents a once-in-ageneration change to the structure of regulation of insurance governance and prudential and risk management. It is probably the most economically rational regulatory approach ever implemented in financial services. Its coincidence to the introspective aftermath of the most severe financial and economic crisis in 75 years will lead to a conservative approach by both firm managers and certainly supervisors to the implementation of that regulation. But Solvency II represents a commercial change as much as a regulatory change; in effect, it is setting out a leading commercial approach to managing an insurer, codifying it and allowing deviation from it for smaller and simpler insurers. Each firm must decide where to position itself in terms both of the regulatory/ commercial continuum and, if at the ‘commercial’ end, their preferred sophistication (especially analytically), which must reflect their risk strategy. The effort required to realise that positioning will need to last well beyond the ‘go-live’ date for Solvency II.

Management actions To ensure the firm’s Solvency II programme is focused on delivering improved commercial decision-making (in addition to regulatory compliance), the CRO and Solvency II lead should review – or retain suitably experienced advisors to review – the programme in the following areas.

Table 1 Areas for management review in the Solvency II programme Topic

Focus of review

Enterprise focus

that the Solvency II programme covers material risks all areas of the firm and will enable the firm to derive the economic (and regulatory) capital implications of those risks at varying levels of severity of impact (including economic scenarios) and include these in decision making

Market data

that the firm will have access to comprehensive market data for asset valuation and for balance-sheet-wide stochastic analysis and stress testing through appropriate data acquisition and warehousing

Integrated stochastic modelling

that the firm’s stochastic modelling will support integrating both sides of the balance sheet to optimise asset allocation to support capital-efficient ALM and that the firm has the computational capacity to undertaking and apply that modelling in timeframes that will be realistic for decision-making

Automated system interfaces

that the firm’s Solvency II programme will reduce manual intervention in analysis and reporting of risk to the minimum level necessary and reduce or eliminate manual transfer of data between systems

Allocation systems

that the firm’s Solvency II programme will ensure allocation and attribution systems apportion risk and cost in an economically relevant way to minimise distortions in decision signals and incentives

To take commercial advantage of the structural, analytical and behavioural improvements forced on them by regulatory fiat, firms must be attentive to their risk strategy and the competencies required to implement it – the firm’s knowledge and skill gaps and how to close them. In regulatory implementations, knowledge requirements are often an after-thought (because they seldom matter commercially). In the new economically rational insurance world, risk and capital knowledge (and franchise value and distribution capacity) will be power.

Greater than the sum of the parts > Executive summary

Table 2 Key issues and resolutions provided by Netik Issue

How Netik addresses the issue

Data coverage for all asset classes



Netik InterView offers comprehensive coverage of on-exchange traded asset classes

Data standards including systems and procedures for data integrity



Netik InterView provides an essential element of data integrity process by offering full data reconciliation and conflict escalation functionality as well as comprehensive audit trails on all data elements

Data warehousing / access to the data



As a best-of-breed solution, Netik InterView is recognised throughout the asset management industry as a leading data warehouse application offering efficient and intuitive access to all data

Enhancing analysis of tail risk – moving beyond the limitations of VaR



By providing an efficient data store across asset classes with infinitely expandable storage capacity, Netik InterView offers users a platform for building a long-term view by asset class supporting extended-duration analysis of volatility and tail events; wide asset class capture offers users a broad basis for stress and scenario testing

Dealing with the computational effort required to support stochastic analysis across both sides of the balance sheet



Offering efficient interfaces (APIs) to all industrystandard modelling applications, Netik InterView reduces the manual intervention required to support high-capacity stochastic analysis

Improved alignment of management of assets and liabilities and risk analysis at portfolio level – asset and liability management



Netik InterView’s comprehensive asset class coverage and efficient data access protocols via the Information Portal minimise the effort required to manage the data side of ALM supporting improved algorithmic and stochastic optimisation routines

Data-centric governance – developing the firm’s analytic core from data > models > capital requirements and demonstrating application in decision-making



By providing a best-of-breed platform for asset data sourcing and use in the firm, Netik InterView’s Data Warehouse and Information Portal make data management a technical concern rather than a weak link in the governance chain. Backed by fully-automated audit trails, Netik InterView’s functionality offers executives and directors the confidence to focus on their addedvalue piece – interpreting analysis of the world as it is and making their decisions based on accurate market data.

Solvency II governance, investment & data implementation issues

Introduction The Solvency II path, begun a decade ago by the European Commission, comes in to force in 2013. But for all insurers, whether opting for use of the standard formula for solvency capital requirements (SCR) under Pillar 1 or developing full or partial internal models, its force is already being felt. In the UK, the changes to the regulatory regime for insurers are less than for many of their continental European counterparts. The FSA’s front-running of Solvency II – the Individual Capital Adequacy Standards (ICAS) regime – introduced in 2004 forms the basic framework for Solvency II. ICAS has given UK insurers a foretaste of Solvency II and many of the terms are the same – the ‘use test’, for example, is part of the ICAS lexicon. But the similarities, while beguiling, are deceiving. Under Solvency II, there are material changes to the technical provisions and bases for preparation of balance sheets. Here again, alignment of UK GAAP with IFRS means that the changes for UK firms may not be as material as for insurers reporting in other European jurisdictions. But they remain material nonetheless.

Much has been written already on the technical changes brought about by the details of changes to technical provisions under Pillar 1. As with Basel II before it, the rigours of implementation have led to considerable focus on the detail of solvency capital calculations in firms’ comfort zones – in banking, that was market and credit risk; in insurance, it is the technical provisions relating to firms’ underwritten risk or insurance liabilities. Because these risks are at the core of insurers’ existing actuarial expertise, this is not where insurers will feel the greatest impact. It will be in the areas that have not been thought of as ‘core’ that firms will face the greatest challenges of implementation and subsequent operation – from, among others, the ‘use test’. High on the list, if not at the top, will be firms’ investment activities, which have often been managed in a silo, separate from the insurance activity of the business.

Those who cannot remember the past are condemned to repeat it. Jorge Sanatyana y Borras

This paper will focus on the area in which Solvency II will have the greatest management impact: what the changes will mean for firms’ management of the asset side of their balance sheets and the data they will need to support their investment activities.

Greater than the sum of the parts

How different from ICAS? The FSA ICAS regime required insurance firms to consider risk on the asset side of the balance sheet just as does Solvency II. Indeed the risk level chosen in Solvency II – capacity to withstand events up to 99.5% percentile over 1 year – was the same in ICAS. If it is the same in Solvency II as for ICAS, what has changed? It is tempting to assume that, with the coverage and the numbers lining up with the previous ICAS regime, not much needs to change. That would be a mistake for several reasons: 1. With insurers able to elect to pursue full or partial internal models for risk under Solvency II, the Pillar 1 calculation forms the basis of the firm’s regulatory capital requirement. Therefore, to ameliorate their own risk, supervisors will review firms’ own Pillar 1 calculations considerably more carefully – supervisory attention to ‘the numbers’ will be enhanced.

Firms will face the biggest knowledge and technology challenges in implementation on the asset side of the balance sheet

2. Under ICAS, the use test requirements were not scoped explicitly as they are under Solvency II and were not, in practice, rigorously applied. Supervisory attention to the ‘governance’ requirements will be significantly enhanced relative to the current regime. 3. Models will be subjected to detailed review before approval and will be expected to provide the ‘right’ answer in firms’ own assessments – other models being used as the basis for decision-making will not be tolerated. 4. Whereas ICAS was a periodic requirement, internal model and ORSA requirements make demonstrating the applicability of the firm’s economic capital model effectively a continuous process.

5. Firms will need to be clear about the different assumptions between their regulatory capital and economic capital models and use a single model system as the base for both. 6. Data quality standards are considerably more stringent than in the past across all areas of the firm’s business. Firms will need to put robust systems in place to ensure data integrity and will, in effect, need to be able to demonstrate audit traceability of data from end-use (decision) to origin. 7. Documentation requirements under Solvency II are considerably more arduous than any previously required. The logic given is to protect the organisation from the impact of loss of key individuals – ‘key person risk’ – to allow an incoming party to pick up where the key person left off without undue disruption. In this paper, we will review some of the key features of Solvency II focusing on the asset side of the balance sheet – where firms will face the biggest knowledge and technology challenges in implementation. The first decision the firm must make is whether to position internally the imperative of Solvency II.

The imperatives of Solvency II Insurance firms have no choice but to comply with Solvency II. For larger and more complex firms, or those pricing more complex risks, both capital and insurance analysts and markets will expect the firm to develop its own internal model – in full or part – in order to ensure sound and sustainable pricing and to optimise the asset and liability positions of the firm. In essence, firms face two imperatives from Solvency II:

Solvency II governance, investment & data implementation issues

 the regulatory imperative for impos-

ing and enforcing minimum capital standards, governance and risk management routines on insurers

 the commercial imperative of link-

ing the business decision-making of a risk-pooling firm to the economic capital position of the firm and the risks that can threaten the solvency of the firm

How the firm approaches the trade-off between these imperatives will impact materially the value it realises from its spend on compliance with Solvency II. This decision will be informed by the firm’s risk strategy and competitive position in the insurance market. Initially, it will be felt in how the firm responds to the greatly increased data standards of Solvency II.

Data standards The explicit requirement that data be robust and appropriate occurs in two separate sections of Solvency II. Article 82 requires that firms “have internal processes and procedures in place to ensure the appropriateness, completeness and accuracy of the data used in the calculation of their technical provisions” for underwritten liabilities. Subsequently, Article 121(3) requires that “[d]ata used for the internal model shall be accurate, complete and appropriate,” applying substantially the same test to both sides of the balance sheet. EIOPA’s final advice on data quality for technical standards notes (at para. 2.2) that the definitions of data quality requirements are consistent across applications. Firms will need to be attentive to and confident about the provenance of their data. Establishing and maintaining its integrity with be a significantly more demanding task under Solvency II.

In implementing Solvency II – whether focusing on a compliance or a commercial imperative – the firm must place data and systems and procedures to ensure data integrity at the centre of its initiatives. The objectivist logic of Solvency II is clear: insurers must develop a datacentric management approach that links data on its internal positions (liability and market data) with the marginal decisions the firm makes on strategy, on underwriting risk and on managing its investment portfolio. Insurers’ external asset managers will not, usually, be able to provide underlying data on risk exposures – they don’t own the data and cannot pass it outside their firm. Insurers will be on their own for risk data on the asset side of their balance sheets for looking through compound and alternative securities in their investment portfolio such as exchangetraded funds and fund of funds instruments. In the Solvency II world, there is no choice about having reliable, near-realtime access to market data history and reliable reference data, only how you achieve it. Insurers can acquire asset managers, build asset management capability or build risk management capability around assets. Whichever approach the firm takes, it needs reliable market and reference data. For undertaking robust variance-based and scenario-based stochastic analysis on the asset side of the balance sheet, there is simply no choice.

Firms will need to be attentive to and confident about the provenance of their data. Establishing and maintaining its integrity with be a significantly more demanding task under Solvency II.

Greater than the sum of the parts

A framework for Solvency II Pulling together the different strands of regulatory attention in the Solvency II puzzle reveals an ‘analytic core’ that emphasises the relationship between data, internal models and prudential capital. If the firm is to realise commercial

Figure 1 Solvency II infrastructure model: the firm’s ‘analytic core’

advantage from its Solvency II initiatives, building or strengthening this analytic core from data to decision-making must be at the heart of the firm’s efforts. We show this in Paradigm Risk’s Solvency II infrastructure model (figure 1, adjacent). This model illustrates that reliable and accurate market and reference data and internal data on liabilities (underwritten risks) and operating data is the starting point for robust modelling which forms the basis of capital target-setting, allocation and capital pricing (charging) in the firm. Reliable and accurate data is the bedrock of effective risk management. Insurers have not typically held the historic data required to support risk analysis of assets nor have they been able to see through the securities held on their behalf to their underlying exposures. Solvency II means they will need to do so. In order to deploy the analytic core, the firm must build the infrastructure around its analytic core to support use of risk analysis in decision-making and to drive the firm’s risk-taking behaviour. Key elements of the infrastructure are  risk systems  risk strategy of the firm  governance of risk in the firm

Figure 2 Solvency II infrastructure model: the firm’s system of control

These form the concentric layers of the Solvency II infrastructure model and are vital both to effective regulatory implementation and to realisation of commercial value from the firm’s inevitable implementation spend. The firm’s risk systems provide the structure for operational and financial control around decision-making. Portraying the firm’s risk system in this way, illustrates clearly that the firm’s data architecture is central to its risk man-

Solvency II governance, investment & data implementation issues

Figure 3 Netik’s Solvency II Architecture: Netik InterView

Solvency II places responsibilities for understanding risks on both sides of the balance sheet – including the asset side – firmly on the shoulders of directors and executives. All insurers have assets so, from a risk perspective, all insurers are ‘asset managers’. Solvency II requires insurers’ boards of directors and executives to form and apply – the “use test” – an integrated perspective on the firm’s risk across each side of the balance sheet – for assets and liabilities. The model shows what Solvency II assumes: in an environment where risk governance assumes increasing importance, reliable data is a governance imperative. Where firms’ boards must show use of analytic models in decision-making – the ‘use test’, sound data and efficient and robust data architectures are board-level problems – if the firm doesn’t have them, the board has a problem. Here, buzzwords can get in the way of clear understanding of requirements. Put simply, the data-centric approach and its link to decision-making (which we have called the ‘objectivist logic’ of Solvency II) is at the heart of the behavioral changes that Solvency II will force firms to adopt and continually operate. The objectivist logic of Solvency II changes

Solvency capital requirements

Minimum capital requirements

Regulatory capital ratios

REPORTING / DECISION MAKING Netik InterView Information Portal

INTERNAL MODEL

Economic capital model

Regulatory capital model

Portfolio replication

Model inputs Expert opinion

Economic scenario generator

Data lineage

Exception-based workﬂow & monitoring

Governance imperatives

Economic capital requirements

Management reporting

Data governance & traceability

agement capability. A critical element of the firm’s risk management infrastructure is the underwriting and investment systems the firm uses to provide the platform for limit-setting as risk limits are deployed throughout the firm. These are an essential aspect of driving behaviour throughout the firm that is consistent with the firm’s risk strategy. Hence, effective control over systems and data is an essential and a vital aspect of robust assurance in the firm. This is particularly true for data sourced from outside the firm and for systems supporting investment decision-making and the firm’s asset and liability management processes.

DATA QUALITY / AGGREGATION / STORAGE Netik InterView Data Warehouse

Proprietary data sources

Other external data sources

Internal data sources

the behaviour and decision logic of the firm towards a data-centric approach. Or, in jargon, the data-centric approach and its link to decision-making . . . is at the heart of the cultural changes that Solvency II will force firms to embed. The objectivist logic of Solvency II changes the risk culture of the firm towards a data-centric approach. But, as the datacentric model illustrates, that only works if the underlying data are sound.

Greater than the sum of the parts

Focus on measuring investment risk The FSA’s analysis of results of QIS 5 for UK insurers shows that the greatest areas of risk for non-life firms and life firms relate to default risk and market risk respectively.

Figure 4 Risk areas for UK firms in QIS 5 Non-life UK companies *

UK life companies *

percent of the time tells you absolutely nothing about what could happen the other 1 percent of the time.1 The dilemma is well summed up by one of the fiercest early critics of the Basel II framework, Jon Danielsson of LSE: As the financial system becomes more complex, the need for complicated statistical models to measure risk and to price assets becomes greater. Unfortunately, the reliability of such models decreases with complexity, so when we need the models the most they tend to be least reliable.2 Or, to put it another way The ultimate irony about VaR-based risk management: when it is needed most is precisely when it performs most poorly.3 The criticisms of VaR are essentially fourfold:

* eliminating diversification benefits

These results show the critical importance of accurate estimation of market risk, especially for firms adopting full or partial internal models. Market risk calculations use the well-established value-at-risk or VaR approach, which is a standard element of both accounting valuations and market practice. However, VaR is not without both its challenges and its critics. Many think misapplication or over-reliance on VaR was behind many of the risk management failures leading to the recent financial crisis. Reliance on VaR leads to considerable attention to the distribution of results up to the tail ‘exceedence’ point. Using a 99% example (versus 99.5% in Solvency II), the problem with VaR is not what happens within the 99% probability; it is about what happens in the other 1 percent, at the extreme edge of the curve. The fact that you are not likely to lose more than a certain amount 99

1. VaR focuses attention on the wrong part of the distribution – most of the focus is on the part of the correlation that does not represent extreme events, whereas firms’ risk functions should be focused on highest impact events in the tail of the distribution 2. VaR underestimates actual risks because:  duration4 over which comparable

data are available. Data series or reported instrument pricing are seldom stable over long periods (known as ‘non-stationarity’)

 data present ‘fat-tails’ (known

as lepto-kurtosis) relative to the best-fit distributions, exhibiting an unexpected number of highimpact events

 correlations change in periods

of financial stress and “decay to 1” – that is, when the market is stressed, values move in increasingly correlated ways and, under extreme stress, liquidity decreases

Solvency II governance, investment & data implementation issues

simultaneously across a wide range of instruments 3. firms and supervisors have relied excessively on VaR relative to tail measures 4. reality is ‘non-Gaussian’ – it does not conform to the distributions or calculated using historic data: The basic problem is that historical data are of limited use in predicting future moves in the markets, especially extreme ones. Almost all statistical models are basically extrapolative; the take past data and project forward from it. Under some circumstances, this can work; under others, it can prove disastrous.5 VaR’s enormous popularity is its simplicity. Whether marking asset values to market or market proxy data (i.e. marking to model), VaR requires access to highquality, comprehensive current market data and extensive historic data series in the asset class or comparable classes to support robust back-testing. However, to ensure that they compensate for the short-comings of VaR, firms need to supplement it with scenarios of price variance and stressed correlations. The recent financial crisis has highlighted the importance of testing the adequacy of firms’ capital of under a range of alternative, stressed economic scenarios covering, for example:  changes in asset values of varying

durations

 yield curve inversion and secular

curve increases/decreases

 liquidity shocks  counterparty failure and/or payment

delays

 deterioration in correlations between

asset classes

 extreme levels of loss experience

In addition, regulators increasingly advocate ‘reverse stress testing’ – understanding the range of conditions under which the firm reaches a distressed state. This technique is particularly important for:

 understanding model limits and

models’ ‘tail’ performance

 defining trigger conditions  understanding how the firm can and

should react under stress

While VaR is expected and necessary under any internal modelling approach for Solvency II, firms must be aware of its limitations and seek to correct for them. That will require not only appropriate reporting and models based on well-understood risk applications, but also robust infrastructure comprising (i) adequate computational capacity and (ii) access to high-quality and reliable data. One of the key lessons from the financial crisis is that robust approaches to information architectures at firm and at system level are indispensible. The crisis has shown there is no such thing as effective risk management without reliable data covering:

VaR is not without both its challenges and its critics. Many think misapplication or over-reliance on VaR was behind many of the risk management failures leading to the recent financial crisis.

 reference data  accessibility of internal data on un-

derwritten and operational risks

 market data  external or third-party data and  counterparty data

Many firms currently face severe constraints not only over access to and storage of high quality market data, but also from their internal capabilities and analytic capacity. In meeting the demand for full Monte Carlo scenario simulation across the whole balance sheet, the technological aspects of the models have become critical.6 Here firms face two options: 1. increasing the computational capacity through addition high performance computing resource to reduce run-times on simulations across the whole balance sheet, and

Whether marking asset values to market or market proxy data (ie. marking to model), VaR requires access to highquality, comprehensive current market data and extensive historic data series in the asset class or comparable classes.

2. reduce the computational load of stressing the balance sheet through use of ‘replicating portfolios’ or a pool of assets designed to reproduce (replicate) the cash flows or market values of a pool of liabilities across a large number of stochastic scenarios

Greater than the sum of the parts > Focus on measuring investment risk

Applying the firm’s investment risk analysis capability offers opportunities in two areas: i. developing replicating portfolios to reduce the computational load of analysis of economic scenarios across the balance sheet and ii. increasing the firm’s capability to (develop and) exploit the opportunities of insurance-linked securities, which are likely to become increasingly important source of event-contingent capital.

Application 1: Replicating portfolios A replicating portfolio is a “proxy portfolio consisting of standard capital-market products that replicate the scenariodependent payoffs of the insurance company’s liability”.7 In addition to reducing computational load of stochastic modelling, use of replicating portfolios allow insurers to isolate the unique risk factors in their balance sheet. The replicating portfolio divides

Figure 5 Illustrating replicating portfolios

the balance sheet in to investment risk and insurance risk which is represented by the replicating portfolio. By isolating the market risk elements of liabilities using a replicating portfolio – the impact of movements in market variables such as interest rates, options values and inflation risks, the firm can identify the ‘pure’ insurance risk it holds – uncertainty of unhedged loss levels (imperfect replication), model risk, risk of distress-related costs and any illiquidity premium on the firm’s capital. Other risks – credit risk, operational risk, and mortality risk – must still be aggregated using correlation matrices or copulae approaches (combining Gaussian distribution functions mathematically). This allows the firm to establish readily a proxy for the rate of return to the insurance book: it is simply the rate of return on the replicating portfolio. All other return represents reward for taking financial risk in the investment portfolio. This provides rapidly a proxy indicator for performance under stress and scenario testing. Because of its ease of computation, use of the replicating portfolio allows the firm to monitor daily market changes in the value of the firm’s liabilities and the assets backing those liabilities and to assess any implications for risk-holding or prudential capital levels. This analysis can be split in to the type of financial risk in the portfolio:  strategic/tactical market risk

Separating market across the balance sheet risk from ‘pure’ insurance risk

 hedging risk  non-hedgeable market risk – such as

long-dated

Liabilityreplicating portfolio

Investment portfolio

source: Western Asset 2011

Liabilityreplicating portfolio

Investment portfolio

= =

Insurance risk

Market risk

 non-market risks

The use of replicating portfolios has grown considerably in Europe since their widespread introduction in the middle of last decade. Recognition within Solvency II of the use of replicating portfolios is likely to accelerate that trend. CEIOPS advice requires that markets in which such instruments eligible to be considered for use in replicating portfolios must

Solvency II governance, investment & data implementation issues

CASE STUDY Netik InterView Netik is assisting one of the UK’s leading life assurance, pensions and investments companies on their Solvency II reporting initiative. The company concerned manages assets of in excess of £150 billion. It is widely understood and reported that effective risk management begins with the integration and management of high quality data. By adopting a proven data-centric approach to managing risk, firms minimise manual effort and reduce the number of errors normally found in a federated or silo-based data framework. After a thorough evaluation of the vendor space for this type of solution, Netik was considered and consequently selected as the best functional and technical fit to provide both products and subject matter expertise. An aggressive implementation timeframe has been placed on the project to deliver on-time, on-budget. The main goals of this important Solvency II initiative are to provide a solution that will enable and facilitate the following key requirements: • The provision of asset data and compliance with Solvency II’s data quality and assurance standards. • Offers analysis and reporting across the investment administration and asset management functions. • Allows for a single source of asset data across the finance and investment accounting operations. • Provides for management information on the undertakings of the whole investment administration function. The Netik solution to meet these requirements is based on Netik InterView, a data management and warehouse solution for the financial services industry. Netik InterView is widely used by fund administrators, custodians, asset managers, prime brokers and hedge funds to consolidate and report on financial data. As such it has been ‘battle tested’ in terms of functional fit, scalability and security. Netik InterView automates the data management process and the enabler of this is the Data Portal, which provides data integration and workflow capabilities. The Public Interface makes it easy to map data to the

data warehouse and enables choice in ETL tool. Netik’s Information Portal enables the ‘visualisation’ of current and historic data. The Netik Information Portal offers secure, true thin-client reporting in multiple formats, in multiple locations at any time. Inquiries provide business users with flexible slice-and-dice reporting that can be customised without assistance from IT. Bespoke reports can also be developed in Crystal and MS Reporting Services. A SQL Server Analysis solution also provides leading-edge OLAP data aggregation and query capabilities. Netik InterView is the main aggregator for, but not limited to, the following types of data: • Market reference data across all asset types (including property, derivatives, schedules, ratings, etc.) • Accounts, customers, issuers, counterparties and parties of interest, etc. • Portfolio, fund data and fund relationship data • Asset and net asset valuation data • Securities lending (collateral) data After data consolidation happens across multiple internal and external sources, a consistent and normalised set of data attributes are used to feed the risk models that will in turn ultimately drive the Solvency Capital Requirements (SCR) and Minimum Capital Requirements (MCR) reports. The client will accrue the following benefits: • Netik provides a best of breed financial data model that is proven and accelerates the time to market when deploying this type of solution. • Netik’s solution comes with out-of-the-box integration capabilities that simplify the mapping of data from multiple source systems. • Netik’s solution is delivered with a standard set of inquiries for reporting purposes. • Netik’s technology platform uses industry standard components and is based on an open architectural framework. • Netik offers exceptional domain expertise, having staff that understand the business problem and have a strong reputation to deliver, thereby minimising project risk. • Netik’s Global Operating Model is there to support our clients on a 24x7x365 basis

Greater than the sum of the parts > Focus on measuring investment risk

be “deep, liquid and transparent on a permanent basis”. In assessing the performance of replicating portfolios, ratings agencies focus on the frequency of review and goodness of fit of the models (models of other liability-related models) to changes in parameters impacting value of liabilities* particularly under extreme conditions, back-tested over “appropriate” historical periods.

The changes introduced by Solvency II will limit firms’ ability to subsidise unprofitable underwriting with returns from higher-risk investment strategies and will force more careful attention to the relationship between the asset and liability sides of the balance sheet.

In firms’ initiatives to respond to the Solvency II requirements for increased analytical diligence around internal models, replicating portfolios will be a valuable weapon in the armoury. However, with strict requirements enforced by supervisors, access to a full spread of current and historic market data will be essential. Here, again, the firms will need to adjust to the new environment of model and data diligence and increased supervisory intensity.

However, overall, the pressures for growth in the ILS market are compelling:  wealth effects – “property values in

geographical areas prone to catastrophic risk,” ie. the rising value of insured stock

 increasing access to reinsurance mar-

ket in higher risk areas in emerging markets

 additional climatic volatility caused

Application 2: Convergence and hybrid instruments The second application of greater knowledge of securities and investment opportunities outside managing the firm’s investment portfolio is for property insurers, where pressures are growing for convergence of insurance, reinsurance and capital markets and use of alternative risk transfer structures. Although the market in insurance-linked (event- and index-linked) securities has grown significantly since the mid-1990s, it has largely underperformed expectations. The issuance of catastrophe bonds peaked at USD 7.0 billion in 2007 following insurers’ heavy losses in the hurricane seasons of 2004 and 2005, but fell back sharply in response the financial crisis. Issuance of cat bonds recovered strongly in 2010.8 Explanations of the restricted growth rate vary from problems with moral hazard and basis risk9 to the observation that the market simply has not reached a critical mass or ‘tipping point’ beyond which insurance-linked securities (ILS) would become a widely invested asset class.10

The prospects for growth of ILS would appear to be very positive given the prevalence, magnitude and losses associated with recent events – most notably recent earthquake losses in New Zealand and Japan – and the string of heavy insurance losses during first decade of the century. For example, three of the ten largest earthquakes ever recorded have occurred in the last decade. Of course, despite this, it is difficult to say that there is a secular increase in earthquake activity or intensity.11

by effects of increasing global temperatures

 increased knowledge in the insur-

ance industry of event-linked or loss or other index-linked structures for securities

 search for yield in buy-side firms such

as hedge funds and mutual funds12

Solvency II stipulates conditions for recognition transfer of risk to SPVs. In doing so, the Directive expressly recognises financial instruments such as ILS and derivatives as risk mitigation techniques eligible for regulatory capital credit. With regulatory recognition, access to capital markets by insurers will be driven by strategic interest and their ability to understand and interpret financial market data matched to underwritten risks. By requiring firms to increase their attention to financial markets and requiring greater diligence in acquisition and deployment of financial market data for valuation and ALM purposes, Solvency II creates the knowledge-related conditions for increasing the rate of convergence between insurance and insurance-linked capital products.

using correlations and ‘the Greeks’ representations of sensitivities of the instruments and derivatives to changes in parameters impacting value – interest rate (rho), volatility (vega) and time to expiry (theta)

Solvency II governance, investment & data implementation issues

Insurance risk management – the bigger picture With limited exceptions only, insurers are regarded as relatively low-risk, low-return stocks. This has clear implications for their aggregate risk-holding: The largest group of insurers appear to adopt a relatively low-risk (low return) strategy, with small (but positive) values of the market beta. Thus most insurers demonstrate returns which follow the market-wide insurance cycle, and share in the market volatility. 13

SUPERVISORY

LEG. & REGULATORY

While the results of QIS 5 show that counterparty risk is a major consideration, especially for non-life companies, the primary risks investors take with insurers are ‘pure insurance’ and market risks. The changes introduced by Solvency II will limit firms’ ability to subsidise unprofitable underwriting with returns

 At- or near-real-time access to current

and historic market data

 Greatly increased stochastic model-

ling functionality through either increased computational capacity (through high-performance computing) or use of replicating portfolio approaches.

Figure 6 Solvency II timeline

L2 implementing measures formally adopted by EC

EC introduces new risk-sensitive capital requirements

EIOPA submits ﬁrst technical standards to EC

EIOPA to publish criteria for 3rd country equivalence

Proposed deadline for technical standards (Om. Bill)

EC – implementing measures for Directive agreed EIOPA submits " L3 ﬁnal adopted guidelines

Proposed deadline for technical standards (EU Parl.)

EIOPA publishes EU / EEA wide stress tests

IAIS to develop common framework for supervision

QIS 5 results published

Jan 2011

FSA opens ﬁrst round IMAP

July April

FIRM-LEVEL

from higher-risk investment strategies and will force more careful attention to the relationship between the asset and liability sides of the balance sheet. Improving asset and liability management (ALM) and increased responsiveness of stochastic modelling to support decision-making will underscore the importance of understanding how assets and liabilities vary dynamically over time under a range of plausible scenarios. But that requires

Jan 2012 Sept

FSA ﬁrst dry run > Oct 2011 EIOPA launches EU / EEA wide stress tests

July April

FSA second dry run

FSA wound up

FSA closes ﬁrst round IMAP

Jan 2013 Sept

Solvency II go live (current) IMAP application window

April

Solvency II go live (proposed in Om. Bill)

Greater than the sum of the parts > Investment risk management

Getting the job done The Solvency II agenda is focused on requiring firms to enhance their risk management performance to a consistently high level. But there are also material risks in getting the firm to that level. The tight timeframe of Solvency II (see timeline in figure 6) leaves little or no room for error, especially in system changes and building new, flexible system interfaces. These exigencies will also drive choice of technology and technology provider: bullet-proof is the new standard. Despite uncertainty over final technical standards and even implementation deadlines, implementation failures, in which Solvency II projects over-run on time, on cost or under-deliver against specification are not an option.

Selecting data warehouse and application providers with a track record of on-time and on-budget implementation and the capacity to deliver and install well-defined applications will reduce implementation risk.

However, to secure commercial benefit, insurers will need to extend their current risk capabilities or enhance their current performance levels or both. This cannot be achieved without close attention to data transfer requirements and systems integration. Flexible interfaces (APIs) which are proven in the field with the insurer’s existing transaction and backoffice systems will be the order of the day. Given the demands of Solvency II implementation agendas, selecting data warehouse and application providers with a track record of on-time and on-budget implementation and the capacity to deliver and install well-defined applications will reduce implementation risk. But the number of Solvency II initiatives underway across the industry and in each firm means that tight implementation control will be vital; even so, slippage under such conditions is almost inevitable. Firms’ implementation plans should allow adequate time for such slippage. Phased implementation, with progressive take-up of internal model elements over time may reduce risk and allow firms better to manage analysts’ and supervisor’s expectations; some firms may find this a beneficial tactic.

Solvency II governance, investment & data implementation issues

Conclusion The financial crisis has already encouraged many insurers to review their investment strategies and risk assessments over their investment. For insurers, returns from their portfolio of assets have been vital to profitability. Understanding how those earnings can be disrupted and valuations can change, and responding to such conditions should they emerge, are vital aspects of effective risk governance and management. That requires robust risk analysis on their asset portfolios. Insurance can no longer be managing just the risks on the liabilities side of the balance sheet. Some – but still relatively few – firms have committed to improvements in the integration of their risks across the balance sheet. The crisis has also shown firms that extreme events do occur and economic scenarios must reflect the possibility of future extreme events and their impact across the balance sheet. The expectation built in to the use test for internal models and the firm’s ORSA will drive increased use of real-time information from capital markets for monitoring investment values and their impact on economic capital levels. Ultimately, this risk information must be reflected in asset allocations that optimise capital deployed. This will require a move from investment mandates driven by investment benchmarks and duration targets to more interaction between asset allocations and liability decisions.

This will drive increased demand for an integrated approach to stochastic modelling requiring, in turn, greater access to current and historic market data and to computational capacity through access to high-performance computing or use of replicating portfolios, or both. Any cost-effective solution for asset risk data involves establishing a robust data warehouse for historic data storage and retrieval that brings together portfolio data and reference data with current and historic market data to support variancebased and scenario-based risk analysis. This needs to occur in near real-time to support decision-making by risk executives, the senior management team and the board – the “use test”. At its heart, any insurer is a risk manager – it assumes risk from other parties which it either carries or sells on to another party. The essence of Solvency II is building an analytic core in to the risk architecture of an insurance firm. The analytic core requires accurate and reliable data which it links to the firms capital model and prudential capital levels to support risks taken on both sides of its balance sheet. The capabilities required represent the core competencies of an insurance firm and its source of competitive advantage. While it is a regulatory imposition, this suggests Solvency II is a commercial imperative in which firms will underinvest – relative to their equally regulatorilymotivated competition – at their peril.

Greater than the sum of the parts

Endnotes

Firms minimise the risks in which they do not specialise so that they can take more of the risks in which they do specialise.

Joe Nocera, (2009), Risk Mismanagement, New York Times Magazine, January 4, 2009

J贸n Dan铆elsson (2008), Blame the Models, Journal of Financial Stability, 4 (4). pp. 321-328

Peter Boekel et al., (2009), Replicating Portfolios An Introduction: Analysis and Illustrations, Milliman Research Report, November 2009

John Cassidy, (2009), How Markets Fail, (London, Penguin)

Aon Benfield, (2010), Insurance-Linked Securities Market Momentum 2010, available online.

Danielsson (2008), who states:

Neil Doherty and Andreas Richter, (2002), Moral Hazard, Basis Risk, and Gap Insurance, Journal of Risk and Insurance, Vol. 69, pp. 9-24

Financial data have the annoying habit of being of short duration. The statistical properties of financial data change over time, often to a considerable extent, and are influenced by other financial variables as well as the general economy in ways that can be intuitively explained, but are often impossible to model.

10 David Cummins and Mary Weiss, (2009), Convergence of insurance and financial markets: hybrid and securitized risk-transfer solutions, The Journal of Risk and Insurance, Vol. 76, No. 3 11 Allianz, (2011), Allianz Risk Pulse: Natural catastrophes on the rise? March, available online

Cassidy (2009)

12 See, for example, Aon Benfield (2010)

Clive Davidson, (2010), Modelling the asset portfolio for Solvency II, Life & Pension Risk, 29 September

13 Simon Ashby and Stephen Diacon, (2010), Risk Appetite in Theory and Practice, Working Paper, Nottingham Business School

Solvency II governance, investment & data implementation issues

About Netik Netik is a global provider to the securities industry of financial data management and reporting services and products with operations in North America, Europe, Asia, Middle East and South Africa. As a growing and expanding company, Netik seeks to increase the sophistication and calibre of what it produces, as well as provide limitless services that bring together people, product, well-honed process and proven technology in the delivery of leading financial data management. Netik solutions sit at the heart of investment and securities operations at many of the world’s leading Asset Managers, Hedge Funds, Wealth Managers, Private Banks, Prime Brokers, Fund Administrators, Custodians, Investment Operations Outsourcing providers and Investment Banks. Netik’s products are ideally positioned to assist insurers to cope with the more rigorous demands for reference and market data discussed in this white paper. The key products Netik offers are:  Netik InterView a data warehouse for the storage of holistic financial data and

information portal to visualize and report on your data  Netik infiPOINT to manage the presentation and delivery of complex information to

business users  Netik Global Securities Master, Netik GSM, a fully managed data service for

reference and market data  Delta One Data, a comprehensive service for quality, timely and accurate global

index and ETF data To find out more about Netik’s offerings and how they can assist insurers with advanced commercial solutions to their investment data needs under Solvency II, contact: New York:

+1 212 267 8800

London:

+44 (0)20 7293 8400

or email us at marketing@netik.com or visit us at www.netik.com

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