Tag Archives: Aisling Barrett

Considerations for Solvency II variation analysis templates

(Re)insurance undertakings completing their Solvency II reporting are required to submit four reporting templates analysing the variance over the year. The European Insurance and Occupational Pensions Authority (EIOPA) explanatory note on these templates was updated most recently in July 2018 with the updated Implementing Technical Standards and LOG files published in November 2018. In this briefing, Milliman’s Matthew McIlvanna and Aisling Barrett discuss these templates and offer perspective on how to approach them.

EIOPA publishes information request for Solvency II 2020 review

The European Insurance and Occupational Pensions Authority (EIOPA) has published its information request on its proposed changes to Solvency II. This provides some insight into EIOPA’s thinking following the consultation on its proposed changes to Solvency II (summarised in Milliman briefing notes here). 

Interestingly, despite not proposing changes to the risk margin in its consultation papers, EIOPA is including a possible change to the risk margin in the impact assessment. The technical specification for the impact assessment sets out a scenario where the projected Solvency Capital Requirements (SCRs) used in the calculation of the risk margin are multiplied by a factor of 0.975 (“the lambda factor”) compounded by the number of years between the valuation date and the projected date. This adjustment to projected SCRs is subject to a minimum of 50% (which bites at projection year 28). This tapering approach is mentioned in a paper by a working party of the Institute and Faculty of Actuaries that reviewed the risk margin and was included in a submission to EIOPA from the Association of British Insurers.

In relation to extrapolation, an alternative extrapolation method is proposed which affects the Euro yield curve at 31 December 2019 as shown in the graph below. This is one of the five options proposed in the EIOPA consultation paper.

As can be seen, the impact is to reduce the Euro curve after the Last Liquid Point (LLP), but not as materially as some of the other EIOPA proposals that were included in the consultation which involved increasing the LLP. Non-Euro yield curves will also be affected but less materially, as the LLP is later. By comparison the UK yield curve actually increases at later durations using this approach. 

Unsurprisingly, interest rate shocks are proposed which increase the impact of the interest rate down shock for the Euro yield curve. The graph below shows the interest rate shocks that would apply as at 31 December 2019 using the current approach and those proposed in the impact assessment (both based on the alternative base Euro yield curve proposed above). 

As can be seen, the proposed interest down shock would increase the impact of this shock (while the impact of the interest up shock would reduce).

Several other changes are being assessed, including:

  • Reflecting realistic new business assumptions in best estimate expenses
  • Correlation factor between interest rate risk and spread risk
  • Volatility adjustment
  • Dynamic volatility adjustment in standard formula (and internal models)
  • A floor on the interest rate down shock
  • Long-term equity requirements for use
  • Recognition of risk mitigation techniques
  • Non-life Minimum Capital Requirement factors
  • Contract boundaries clarification

Supervisors have notified companies that are required to participate in the impact assessment. Submissions are due 31 March 2020. EIOPA’s final opinion on the 2020 review of Solvency II is due in June.

Analysis of Solvency and Financial Condition Reports for Irish life insurers

Solvency II came into effect on 1 January 2016 and introduced a number of disclosure requirements for European insurers. Under the new rules, European insurers are required to publish a Solvency and Financial Condition Report (SFCR). The third set of SFCRs contains a significant amount of information, including details on business performance, risk profile, balance sheet, and capital position. Insurers are also required to publish quantitative information in the public Quantitative Reporting Templates (QRTs) included within the SFCRs. This analysis by Milliman’s Matthew McIlvannaSinéad Clarke and Aisling Barrett highlights some interesting information published in the SFCRs of life insurance companies in Ireland at year-end 2018, focussing on premiums, investments and solvency coverage.

Overview of EIOPA’s 2020 Solvency II review consultation

The European Insurance and Occupational Pensions Authority (EIOPA) recently published its second wave of consultation regarding the 2020 Solvency II review. The deadline for feedback is 15 January 2020 and the deadline for those participating in the information request is 6 December 2019. This briefing note by Milliman consultants Aisling Barrett and Eoin King summarises EIOPA’s proposals.

Using predictive modelling in assumption setting

Milliman is carrying out a series of policyholder behaviour experience studies using predictive analytics. This blog post discusses the most recent US-based study looking at Guaranteed Lifetime Withdrawal Benefit (GLWB) utilisation, which, along with lapse, is a key driver of variable annuity (VA) business value.

The study was based on a data set containing around 2 million unique VA policies issued between 2003 and 2015 of seven large variable annuity writers based in the US. These policies represent roughly $220 billion of account value (based on initial purchase amounts) and cover a range of GLWB product designs as well as demographic attributes. This provides a rich data set with which to study policyholder behaviour.

A predictive model can be constructed with common variables such as age, tax-qualified status and single/joint status to allow easy implementation. The models constructed for our study use drivers that are readily available in a typical in-force data file, making them suitable for implementation in existing actuarial projection platforms. Including additional explanatory variables or interactions to the assumption formula is a natural step of predictive modelling because many variables can be captured in a single model without double-counting the individual variables’ effects. This framework allows iterative improvements to predictions and better differentiation of policyholder behaviour at a seriatim level.

The 2016 Milliman VALUES™ GLWB Utilisation study examined both when the policyholders chose to begin taking lifetime withdrawals, as well as how efficiently they continued to take them thereafter. We were able to confirm and, more importantly, quantify many intuitive assumptions about these behaviours and what drives them, and discovered new insights as well. For example, less than half of all policyholders currently taking GLWB withdrawals utilise their GLWB benefit with 100% efficiency (i.e., taking precisely the maximum allowed withdrawal amount). This is interesting as we believe many companies price on a basis of 100% efficiency.

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