The combination of increasing vulnerability and intensity of natural disasters has led to severe humanitarian impacts in recent years. The uncertainty of environmental change and growing populations suggests that this trend is a cause for significant concern. Increasingly, nations are seeking additional methods to increase their financial resilience to disaster events. Sovereign disaster risk financing and insurance (SDRFI) may be the panacea for managing disaster risk at a national level. However, there is a need for better evidence to enhance, guide and support these programmes. The design of SDRFI programmes aim to maximise their impact and reduce the human and economic cost of disasters. This paper presents an overview of 16 technical background papers that offer insights into the development of an impact appraisal methodology for SDRFI evaluation. The papers are part of a joint project between the UK Department for International Development and the World Bank and the Global Facility for Disaster Reduction and Recovery.

This paper brings together the work of the GI Solvency II Technical Provisions working party. The working party was formed in 2009 for the primary purpose of raising awareness of Solvency II and the impact it would have on the work that reserving actuaries do. Over the years, the working party’s focus has shifted to exploring and promoting discussion of the many practical issues raised by the requirements and to promoting best practice. To this end, we have developed, presented and discussed many of the ideas contained in this paper at events and forums. However, the size of the subject means that at no one event have we managed to cover all of the areas that the reserving actuary needs to be aware of. This paper brings together our thinking in one place for the first time. We hope experienced practitioners will find it thought provoking, and a useful reference tool. For new practitioners, we hope it helps to get you up-to-speed quickly. Good luck!

This abstract relates to the following paper: Sovereign disaster risk financing and insurance (SDRFI) impact appraisal, by Tse-Ling Teh.

Tse-Ling Teh, Disaster Risk Financing & Insurance Program, World Bank

This abstract relates to the following paper: DrekslerS.,AllenC.,Akoh-ArreyA.,CourcheneJ. A., JunaidB.,KirkJ.,LoweW.,O’DeaS.,PiperJ.,ShahM.,ShawG.,StormanD.,ThaperS.,ThomasL.,WheatleyM. and WilsonM. Solvency II technical provisions for general insurers (Brian Hey prize) ‐ Abstract of the London Discussion. British Actuarial Journal, doi: 10.1017/S1357321714000099

This abstract relates to the following paper: IFoA/LLMA (2014) Longevity Basis Risk. A methodology for assessing basis risk by Cass Business School and Hymans Robertson LLP. Available at http://www.actuaries.org.uk/sites/all/files/IFoA%20LLMA%20Longevity%20Basis%20Risk%20Report_0.pdf

Common to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.

Our principal focus in this paper is on ways that a Fast Close process (or indeed any reserving process) can be structured to maximise the value added within the process given the time and resource available. This builds on the use of actual vs. expected techniques investigated in our previous paper, and also looks at forces external to the reserving function that may derail smooth progress. We highlight a number of practical ways that the balance can be restored in favour of adding value rather than crunching numbers. This paper forms the second in the TORP series.

This abstract relates to the following paper: AdamsC.Adverse selection in a start-up long-term care insurance market ‐ Abstract of the London Discussion. British Actuarial Journal. doi:10.1017/S1357321714000270

This paper looks at alternative long-term care (LTC) funding models and options. It analyses six global models and the potential applications to the United Kingdom. The paper provides a demographic overview for each of the six countries to illustrate the increasing global demand for LTC and the funding thereof that is becoming a global critical issue. It examines the approaches each country has taken to address the funding issue with varying degrees of public/private co-operation and partnerships. The authors hope that the lessons learned from these other markets will assist the United Kingdom as they try to come to grips with their growing social care funding issue.

This abstract relates to the following paper: ElliottSLong-term care: a review of global funding ‐ Abstract of the Edinburgh Discussion. British Actuarial Journal, doi: 10.1017/S1357321714000300

Life insurers have historically relied upon investment markets as a key source of profit and crucially have been able to do this while embarking on relatively “vanilla” investment strategies. In the current low-yield environment, broadening their investment horizons is critical to maintaining profitability. This paper summarises some relevant external literature and the working party’s own research in understanding the potential benefits and pitfalls for insurers seeking to invest in non-traditional assets. The objective of this paper is to help educate and promote understanding by all (the many) relevant parties. In doing so, we hope to help organisations to achieve some further economic success for the ultimate benefit of society. Although this paper has primarily been written from the perspective of a life insurer, we hope it will be of interest to a much wider audience. Many of the asset classes considered here are relevant to general insurers, pension funds and the wider capital markets. It is very important to note that this paper does not contain investment advice and the analysis represents the views of the individuals and the working party and not the companies which they represent or the Profession. This paper does not make any comment as to the suitability (or otherwise) of specific investments for particular investors.

Critical illness (CI) insurance involves cover that pays out on the diagnosis of an illness which is deemed to be critical. Estimation and graduation of CI insurance claim rates has been challenging, partly because of the diagnosis of the insured event often being unclear or not recorded. This introduces additional uncertainties in the evaluation of claim rates. In this work, which was funded by an Institute and Faculty of Actuaries research grant, we have addressed the issue of model and parameter uncertainty in claim rate estimation, when the date of diagnosis is missing, aiming at obtaining graduated rates that can be applied to estimate the future cash flow of CI policies and determine insurers’ liability more accurately. Better understanding of uncertainty in rate graduation and pricing is important for insurers, not least because of future changes in the interpretation of the “definition” of an illness or advances in medical practice leading to more efficient diagnosis and treatment.

Estimation of claim rates was addressed in this project using a parametric Poisson model for the number of claims, which accounts for claims that have not been settled by the end of the observation. This was achieved by considering probabilities of the distribution of the delay period between diagnosis of insured illness and settlement of the corresponding claim. We have developed appropriate generalised-linear-type models (including lognormal, Burr, generalised gamma and generalised beta) to investigate this delay, using data supplied by the Continuous Mortality Investigation (1999–2005). The analysis includes various claim risk factors (e.g. gender, benefit amount, policy duration and so on) and was performed under a Bayesian framework, using Markov chain Monte Carlo estimation techniques.

Based on this experience, our analysis suggests that estimates of the claim delay distribution are model sensitive, but claim rates and pricing are not. Under the best claim delay distribution fitting, the following risk factors are considered important for predictive purposes: policy duration, selling office, benefit type, benefit amount, policy type and cause of claim.

This abstract relates to the following paper: Mee, G. Non-traditional investments: key considerations for insurers. British Actuarial Journal, doi:10.1017/S135732171500015X