SFCR: Capital Insights

This blog is part of the Pillar 3 Reporting series. For more blogs in this series click here.

Following the first annual reporting deadline under Solvency II, we look at the quality of the Own Funds on Irish company balance sheets.

All companies
The figures below are based on an analysis of 46 Solvency and Financial Condition Reports (SFCRs), which cover all the major players in the Irish insurance market. The headline statistic is that Tier 1 unrestricted Own Funds account for 93.7% of capital on Irish insurers’ balance sheets, as shown in Figure 1. Tier 1 restricted (1.1%), Tier 2 (2.9%), and Tier 3 (0.8%) make up the remainder of basic Own Funds. The small level of ancillary Own Funds (1.5%) shows that very few companies have applied to include additional ancillary items on their balance sheets.

Solvency II_Own Funds Breakdown_All Companies
Figure 1

Life industry
It is useful to consider companies selling life business in isolation. We have included 25 published SFCRs within this category.

Firstly, in Figure 2, we look at domestic life companies selling in Ireland. For these companies, a minimum of 90% of Own Funds is Tier 1 unrestricted capital.

Figure 2

In fact, as seen in Figure 3, all these domestic companies are covering 100% of the Solvency Capital Requirement (SCR) using Tier 1 unrestricted capital.

Solvency II_SCR coverage
Figure 3

We see a similar picture in Figures 4 and 5 for the cross-border life market in Ireland, with very few cases of lower-quality capital on the balance sheet. Again, all the companies examined cover the SCR using 100% Tier 1 unrestricted capital.

Figure 4
Figure 5

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Recovery plans: A natural extension of the ORSA

Recovery and resolution1 plans (RRPs) are becoming increasingly important for insurance and reinsurance companies. A requirement to develop RRPs already applies to global systemically important insurers (G-SIIs) and in some territories we are also seeing requirements coming into force which apply to smaller insurers that have not been classified as G-SIIs. In Europe, for example, the European Insurance and Occupational Pensions Authority (EIOPA) is looking at the area of recovery and resolution planning, with Gabriel Bernardino stating that ‘One of the lessons learned from the recent financial crisis is the need to have in place adequate recovery and resolution tools which will enable national authorities to intervene in failing institutions and resolve failures when these materialise in an effective and orderly manner.’2 This speech was followed by the release of an EIOPA discussion paper on the potential harmonisation of recovery and resolution frameworks for insurers.

This blog post offers a look at the link between RRPs and the Solvency II Own Risk and Solvency Assessment (ORSA).

ORSA requirements
One of the key aims of the ORSA is for insurers to identify and measure the risks that they face, with a view to either holding capital against these risks, or taking steps to manage or mitigate them. This process is called the insurer’s assessment of its overall solvency needs.

Guideline 7 of the Solvency II Level 3 Guidelines on the ORSA covers this assessment. It says that, “The undertaking should provide a quantification of the capital needs and a description of other means needed to address all material risks ….”

The explanatory text of this guideline expands on the factors to be considered by companies in deciding whether to cover risk with capital or to use risk mitigation techniques. These considerations include the following:

• If the risks are managed with risk mitigation or recovery techniques, the (re)insurer should explain the techniques used to manage each risk.
• The assessment needs to cover whether the company currently has sufficient financial resources and realistic plans for how to raise additional capital if and when required.
• The assessment of the overall solvency needs is expected to at least reflect the (re)insurer’s management practices, systems and controls, including the use of risk mitigation techniques.
• When assessing the overall solvency needs, the company should also take into account management actions that may be adopted in adverse circumstances. When relying on such actions, companies should assess the implications of taking these actions, including their financial effect, and take into consideration any preconditions that might affect the efficacy of the management actions as risk mitigators. The assessment also needs to address how any management actions would be enacted in times of financial stress.

Based on some of the ORSA reports that I have seen, companies are generally good at identifying possible risks and projecting their solvency positions allowing for the impact of these risks. Companies are also quite good at using the results of such analyses in determining capital buffers as part of the assessment of their overall solvency needs. Furthermore, as required by Solvency II, companies tend to have capital management plans in place, identifying possible shortfalls in own funds and how they might be addressed. However, some of these plans are often quite vague in terms of companies’ prospects of raising capital in the event of financial distress. In such cases, parents might not be willing or able to provide capital and the investment markets might also prove difficult to access.

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Validating machine-learning models

While machine-learning techniques can improve business processes, predict future outcomes, and save money, they also increase modeling risk because of their complex and opaque features. In this article, Milliman’s Jonathan Glowacki and Martin Reichhoff discuss how model validation techniques can mitigate the potential pitfalls of machine-learning algorithms.

Here is an excerpt:

An independent model validation carried out by knowledgeable professionals can mitigate the risks associated with new modeling techniques. In spite of the novelty of machine-learning techniques, there are several methods to safeguard against overfitting and other modeling flaws. The most important requirement for model validation is for the team performing the model validation to understand the algorithm. If the validator does not understand the theory and assumptions behind the model, then they are likely to not perform an effective model validation on the process. After demonstrating an understanding on the model theory, the following procedures are helpful in performing the validation.

Outcomes analysis refers to comparing modeled results to actual data. For advanced modeling techniques, outcomes analysis becomes a very simple yet useful approach to understanding model interactions and pitfalls. One way to understand model results is to simply plot the range of the independent variable against both the actual and predicted outcome along with the number of observations. This allows the user to visualize the univariate relationship within the model and understand if the model is overfitting to sparse data. To evaluate possible interactions, cross plots can also be created looking at results in two dimensions as opposed to a single dimension. Dimensionality beyond two dimensions becomes difficult to evaluate, but looking at simple interactions does provide an initial useful understanding of how the model behaves with independent variables….

…Cross-validation is a common strategy to help ensure that a model isn’t overfitting the sample data it’s being developed with. Cross-validation has been used to help ensure the integrity of other statistical methods in the past, and with the rising popularity of machine-learning techniques, it has become even more important. In cross-validation, a model is fitted using only a portion of the sample data. The model is then applied to the other portion of the data to test performance. Ideally, a model will perform equally well on both portions of the data. If it doesn’t, it’s likely that the model has been over fit.

SFCR: Where are the risks?

This blog is part of the Pillar 3 Reporting series. For more blogs in this series click here.

Following the first annual reporting deadline under Solvency II, here’s a look at the breakdown of risk components within the Solvency Capital Requirement (SCR) across the Irish market. This provides a useful insight into the largest drivers of regulatory capital, while also indicating some of the sources of risk for companies.

All companies
This analysis is based on 40 published Solvency and Financial Condition Reports (SFCRs) as only standard formula companies have been included. The graph in Figure 1 shows the breakdown of the various SCR components, where 100% represents the calculated SCR.

As can be seen, underwriting risk represents the largest driver of SCR, followed by market risk. In this case, underwriting risk represents a combination of life, health, and non-life underwriting risks.

The benefits of diversification and loss-absorbing capacity represent an average reduction of 43% of the SCR. Please note that diversification here is at the SCR module level and doesn’t include the impact of diversification across sub-modules.

Figure 1

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Mixed outlook for participating business across Asia

Milliman has released the findings of a study analysing and comparing participating (par) business across seven Asian insurance markets notably Singapore, India, Malaysia, Hong Kong, China, Indonesia and Sri Lanka. The report collates in-depth information not otherwise available and provides insight from survey results about par business in Asia.

“Par products have been a core insurance offering for many decades in many markets across Asia Pacific and in Singapore, Hong Kong and India they remain a cornerstone of the industry” said Richard Holloway, managing director for Milliman’s South East Asia and India life consulting practice. “However, increased regulatory scrutiny of par business in countries such as Malaysia and the onset of risk based capital solvency regimes in most markets may lead to a gradual decline in the popularity of such products. This report unlocks key considerations for companies offering par products across the region, highlighting differences in performance, investment approach, and governance of par across the seven markets.”

The “Milliman Participating Business in Asia” report includes:

• A regional view of common themes and differences between the seven selected markets
• Detailed country commentary on par business performance, regulatory environments and key challenges
• Results of our survey providing qualitative insights into par business in these countries
• Analysis of the governance frameworks in place and roles of policyholder advocates

To download the report, click here.

SFCR: Who’s doing what?

This blog is part of the Pillar 3 Reporting series. For more blogs in this series click here.

Following the first annual reporting deadline under Solvency II, here’s a look at the different approaches being taken by Irish companies in terms of internal models and transitional or long-term guarantee measures.

While this initial analysis does not include every company, the sample includes 46 companies based in Ireland with aggregate Own Funds of €26.4 billion, including all the major players.

Internal models
The identities of the insurance companies using internal models may have been an open secret, but the publication of Solvency and Financial Condition Reports (SFCRs) allows us to confirm them below.

Based on our sample, there are 10 companies using an internal model for Solvency Capital Requirement (SCR) purposes. Interestingly, Ireland has subsidiaries of almost all the major international insurance groups, so what is learned in Ireland also gives an insight into the international market.

# Company Group Full Partial
1 Allianz plc Allianz SE X
2 Allianz Global Life Allianz SE X
3 Axa Life Europe Axa SA X
4 Axa Insurance Axa SA X
5 Axa MPS Financial Axa SA X
6 Beazley Re Beazley plc X
7 Hannover Re (Ireland) Hanover Ruck SE X
8 Prudential International Assurance plc Prudential plc X
9 SCOR Global Life Reinsurance SCOR SE X
10 Zurich Insurance plc Zurich Insurance Group X

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