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 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

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.

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

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.