This paper describes the use of a stochastic model of the full regulatory balance sheet of an insurer under the Solvency II regime. The purpose of the model is to enable firms to understand the key risks that threaten the regulatory solvency of the firm and therefore its long-term business objectives.

The aim of this research was to look at the emergence of wearable technology and the internet of things (IoT) and their current and potential use in the health and care area. There is a wide and ever-expanding range of wearables, devices, apps, data aggregators and platforms allowing the measurement, tracking and aggregation of a multitude of health and lifestyle measures, information and behaviours. The use and application of such technology and the corresponding richness of data that it can provide bring the health and care insurance market both potential opportunities and challenges. Insurers across a range of fields are already engaging with this type of technology in their proposition designs in areas such as customer engagement, marketing and underwriting. However, it seems like we are just at the start of the journey, on a learning curve to find the optimal practical applications of such technology with many aspects as yet untried, tested or indeed backed up with quantifiable evidence. It is clear though that technology is only part of the solution, on its own it will not engage or change behaviours and insurers will need to consider this in terms of implementation and goals. In the first weeks of forming this working party, it became evident that the potential scope of this technology, the information already out there and the pace of development of it, is almost overwhelming. With many yet-unanswered questions the paper focuses on pulling together in one place relevant information for the consideration of the health and care actuary, and also to open the reader’s eyes to potential future innovations by drawing on use of the technology in other markets and spheres, and the “science fiction–like” new technology that is just around the corner. The paper explores:

• an overview of wearables and IoT and available measures,

• examples of how this technology is currently being used,

• data considerations,

• risks and challenges,

• future technology developments and

• what this may mean for the future of insurance.

Under the European Union’s Solvency II regulations, insurance firms are required to use a one-year VaR (Value at Risk) approach. This involves a one-year projection of the balance sheet and requires sufficient capital to be solvent in 99.5% of outcomes. The Solvency II Internal Model risk calibrations require annual changes in market indices/term structure for the estimation of risk distribution for each of the Internal Model risk drivers. This presents a significant challenge for calibrators in terms of:

• Robustness of the calibration that is relevant to the current market regimes and at the same time able to represent the historically observed worst crisis;

• Stability of the calibration model year on year with arrival of new information.

The above points need careful consideration to avoid credibility issues with the Solvency Capital Requirement (SCR) calculation, in that the results are subject to high levels of uncertainty.

For market risks, common industry practice to compensate for the limited number of historic annual data points is to use overlapping annual changes. Overlapping changes are dependent on each other, and this dependence can cause issues in estimation, statistical testing, and communication of uncertainty levels around risk calibrations.

This paper discusses the issues with the use of overlapping data when producing risk calibrations for an Internal Model. A comparison of the overlapping data approach with the alternative non-overlapping data approach is presented. A comparison is made of the bias and mean squared error of the first four cumulants under four different statistical models. For some statistical models it is found that overlapping data can be used with bias corrections to obtain similarly unbiased results as non-overlapping data, but with significantly lower mean squared errors. For more complex statistical models (e.g. GARCH) it is found that published bias corrections for non-overlapping and overlapping datasets do not result in unbiased cumulant estimates and/or lead to increased variance of the process.

In order to test the goodness of fit of probability distributions to the datasets, it is common to use statistical tests. Most of these tests do not function when using overlapping data, as overlapping data breach the independence assumption underlying most statistical tests. We present and test an adjustment to one of the statistical tests (the Kolmogorov Smirnov goodness-of-fit test) to allow for overlapping data.

Finally, we explore the methods of converting “high”-frequency (e.g. monthly data) to “low”-frequency data (e.g. annual data). This is an alternative methodology to using overlapping data, and the approach of fitting a statistical model to monthly data and then using the monthly model aggregated over 12 time steps to model annual returns is explored. There are a number of methods available for this approach. We explore two of the widely used approaches for aggregating the time series.

This abstract relates to the following interim paper: Kenny, T., Golding, C., Craske, G., Dobinson, A., Gunter, S., Griffiths, O., Hayes, N., Mockridge, A., Robertson, S., Saundh, R. and Thorpe, J., Actuarial management of equity release mortgages: current practice and issues in the actuarial management of ERMs in the UK. This paper is available on the Institute and Faculty of Actuaries (IFoA) website at https://ifoa-www.s3.eu-west-2.amazonaws.com/live/s3fs-public/Sessional%20Paper_0.pdf. The final paper will be published in due course.

This abstract relates to the following paper: Hue, B., Jinks, A., Spain, J., Bora, M. and Siew, S. (2019) Investment risk for long-term investors: risk measurement approaches: considerations for pension funds and insurers. British Actuarial Journal, 24, e16, doi: 10.1017/S1357321719000102.

Increasing global concern over the impact of climate change has recently led to public scrutiny over the adequacy of existing risk management practices by insurance companies and pension schemes in dealing with these challenges that potentially impact both individual actuaries and the Institute and Faculty of Actuaries generally. Most recently, the Prudential Regulation Authority has issued further guidance concerning its expectations for the UK insurance industry regarding the development of an approach to disclosure on and management of the financial risks from climate change, while a Parliamentary Committee has demanded public clarification from UK pension scheme trustees regarding their degree of engagement with incorporating climate-related financial risks into their investment decision-making. The aim of this paper is to identify the dominating factors of the current evolvement of UK insurance companies’ and pension schemes’ climate risk disclosure practices. This paper analyses both the nature and extent of changes in the risk reporting practices of these entities that have evolved in order to meet these demands for increased accountability. We first analyse relevant sections of latest annual reports produced by a sample of 15 UK insurance companies and 15 pension schemes. We find only limited alignment of insurance firm and pension scheme annual reports with the 11 specific Task Force on Climate-Related Financial Disclosure’s (TCFD) recommended disclosures. We also examine what key financial risk and/or other organisational characteristics are most closely associated with the degree of alignment with TCFD specified disclosures related to governance, strategy, risk management and performance metrics. We find that incentives facing sample insurance companies to align their climate-related disclosures with TCFD recommendations are related to their management of reputation risk (measured on the basis of size and type of business). Whereas the incentives facing pension schemes are related to the desire to reduce information asymmetry (measured by liability risk) among their stakeholders concerning this issue. Further, consistent with a stakeholder theory explanation, it appears that only a minority of large, publicly listed insurance companies and large local government pension schemes are taking action to report on their actions to mitigate climate risk. We also discuss examples of best practice climate risk reporting. The implications for the actuarial profession in engaging with climate risk are discussed in line with the findings of the study.

This abstract refers to the following paper: Aggarwal, G., Bird, C., Cox, A., Durkin, T., Kaye, P., Marcuson, A., Masters, T., Regan, N., Restrepo, S., Shah, N., Smith, A., Stock, R., Strudwick, M., Toller, J., White, W., White, S. and Wilkinson, R. Managing Uncertainty: Principles for Improved Decision Making.

Compound interest was known to ancient civilisations, but as far as we know it was not until medieval times that mathematicians started to analyse it in order to show how invested sums could mount up and how much should be paid for annuities. Starting with Fibonacci in 1202 A.D., techniques were developed which could produce accurate solutions to practical problems but involved a great deal of laborious arithmetic. Compound interest tables could simplify the work but few have come down to us from that period. Soon after 1500, the availability of printed books enabled knowledge of the mathematical techniques to spread, and legal restrictions on charging interest were relaxed. Later that century, two mathematicians, Trenchant and Stevin, published compound interest tables for the first time. In 1613, Witt published more tables and demonstrated how they could be used to solve many practical problems quite easily. Towards the end of the 17th century, interest calculations were combined with age-dependent survival rates to evaluate life annuities, and actuarial science was created.