This paper demonstrates how climate scenario analysis can be used for forward-looking assessment of the risks and opportunities for financial institutions, using a case study for a UK defined benefit pension scheme. It uses a top-down modelling tool developed by Ortec Finance in partnership with Cambridge Econometrics to explore the possible impacts of three plausible (not extreme) climate pathways of the scheme’s assets and liabilities. It finds that the funding risks are greater under all three climate pathways than under the climate-uninformed base scenario. In the absence of changes to the investment strategy or recovery plan, the time taken to reach full funding is increased by three to nine years. Given that most models currently used by actuaries do not make explicit adjustments for climate change, these modelled results suggest it is quite likely that pension schemes are systematically underestimating the funding risks they face.

Recent advances in large language models (LLMs), such as GPT-4, have spurred interest in their potential applications across various fields, including actuarial work. This paper introduces the use of LLMs in actuarial and insurance-related tasks, both as direct contributors to actuarial modelling and as workflow assistants. It provides an overview of LLM concepts and their potential applications in actuarial science and insurance, examining specific areas where LLMs can be beneficial, including a detailed assessment of the claims process. Additionally, a decision framework for determining the suitability of LLMs for specific tasks is presented. Case studies with accompanying code showcase the potential of LLMs to enhance actuarial work. Overall, the results suggest that LLMs can be valuable tools for actuarial tasks involving natural language processing or structuring unstructured data and as workflow and coding assistants. However, their use in actuarial work also presents challenges, particularly regarding professionalism and ethics, for which high-level guidance is provided.

Over the course of the 20th century Ireland moved from being a largely young population with a high death rate from infectious diseases to an increasingly older population with a consequent rise in chronic diseases. Understanding the changes that occurred in Irish mortality over the 20th century and how these changes compare with those experienced by similar countries can help us plan for the challenges of our aging population. This paper analyses trends in mortality in Ireland over the period 1901 to 2006 by age group, gender and five broad categories of cause of death – infectious diseases, circulatory diseases, respiratory diseases, cancer and external causes. To place the changes in an international context the trends are compared with those experienced by Northern Ireland and England and Wales. Ireland experienced the fastest improvements in mortality of the three regions in the early years of the 21st century. By 2006 the mortality of Irish males ranked between that of Northern Ireland and England and Wales while Irish females experienced the lowest mortality of the three regions. The improvement in Irish mortality in the 21st century can be attributed mainly to the drop in deaths from circulatory diseases for both males and females.

Sir Andrew Herrick Rowell died on 27 August 1973 in his 84th year. To members of the Institute one tribute overshadows all the rest. He was our Centenary President. It was in June 1948 that the bonds of friendship and affection, so strong among actuaries the world over, but which had been so hard to sustain in the preceding years of war and its aftermath, were gloriously restored with the Centenary Assembly of the Institute of Actuaries in London at which Andrew Rowell, newly knighted by His Majesty, presided so memorably. To his contemporaries the Centenary Assembly was a time for old friends and new resolves. To one young actuary, not yet fully qualified, and he cannot have been alone, it was the kindling of a flame of love of and pride in his profession which still burns brightly today.

This research employs a vector autoregression (VAR) analysis to explore the volatility and dynamic interactions between stock, commodity, and cryptocurrency markets. It focuses on the returns of the S&P 500, gold, crude oil, and Bitcoin to analyse their interconnections. Our results indicate that Bitcoin returns positively affect S&P 500 and crude oil, but negatively impact gold. Conversely, crude oil returns have a positive influence on gold but lead to decreased returns for Bitcoin and the S&P 500. Similarly, higher gold returns correspond to increased returns in crude oil and S&P 500 but decreased returns in Bitcoin. The rise of the S&P 500 negatively influences Bitcoin and crude oil returns, while gold returns remain unaffected. However, these relationships exhibit weak and limited strength. Including these assets in a portfolio can help risk mitigation, as Bitcoin diversifies crude oil, gold, and S&P 500, and crude oil diversifies S&P 500. These findings contribute to our understanding of global financial dynamics and inform decision-making in risk assessment, portfolio management, risk mitigation, and diversification strategies.

We explore some of the risks related to Artificial Intelligence (AI) from an actuarial perspective based on research from a transregional industry focus group. We aim to define the key gaps and challenges faced when implementing and utilising modern modelling techniques within traditional actuarial tasks from a risk perspective and in the context of professional standards and regulations. We explore best practice guidelines to attempt to define an ideal approach and propose potential next steps to help reach the ideal approach. We aim to focus on the considerations, initially from a traditional actuarial perspective and then, if relevant, consider some implications for non-traditional actuarial work, by way of examples. The examples are not intended to be exhaustive. The group considered potential issues and challenges of using AI, related to the following key themes:

• Ethical

  • ○ Bias, fairness, and discrimination

  • ○ Individualisation of risk assessment

  • ○ Public interest

• Professional

  • ○ Interpretability and explainability

  • ○ Transparency, reproducibility, and replicability

  • ○ Validation and governance

• Lack of relevant skills available

• Wider themes

This paper aims to provide observations that could help inform industry and professional guidelines or discussion or to support industry practitioners. It is not intended to replace current regulation, actuarial standards, or guidelines. The paper is aimed at an actuarial and insurance technical audience, specifically those who are utilising or developing AI, and actuarial industry bodies.

This paper describes how statistical methods can be tested on computer-generated data from known models. We explore bias and percentile tests in detail, illustrating these with examples based on insurance claims and financial time series.

The origin of savings connected with human life was not a particularly happy one in France. A certain Neapolitan named Lorenzo Tonti submitted to Mazarin, the Finance Minister of ‘Sun King’ Louis XIV, a savings plan, the profits from which would help to replenish the sadly depleted royal coffers of this grandiose monarch. The plan was called a ‘Tontine’ after its originator and provided for a lump sum to be divided amongst the survivors of those who had contributed after a period of say 10 to 15 years. The ‘sales psychology’ was good as the individual is normally of the opinion that he will outlive his contemporaries. Unfortunately the results were disastrous as Parliament refused to ratify the royal edict of 1653 and Mr Tonti was clapped in the Bastille.

This paper addresses the process of estimating loss reserves for a company or syndicate writing in the London Market. Particular emphasis is placed on insurers maximising the value of the process, and ensuring that the process is not simply a series of mathematical calculations. The use of sophisticated mathematical techniques should not distract from the importance of understanding the business and ensuring that data are correct. Sophisticated mathematical techniques can give rise to misleading impressions of confidence and accuracy to estimates, which are often subject to considerable uncertainty. The principles (rather than the detailed techniques) are illustrated by a case study based on a hypothetical London Market writer. Many of these principles are relevant to other markets.

British life offices transacting life assurance outside the United Kingdom have until now confined themselves almost entirely to the countries of the British Commonwealth—because among the countries with stable currencies the members of the Commonwealth have in the main been the ones with which we have had the largest numbers of other business contacts. Another influence has been the absence of a language barrier. Before the 1914–18 War some offices transacted business on the Continent of Europe but thereafter most of them pulled out; today as far as I know only one British office transacts life assurance in France, one in Germany and none in Italy.

One might be forgiven therefore for thinking that actuaries handling life office affairs need not concern themselves with life business on the Continent of Europe—particularly as, at the beginning of last year, the negotiations for Britain's entry into the European Common Market broke down.