The systemic nature of climate risk is well established, but the extent may be more severe than previously understood, particularly with regard to cyber risk and economic security. Cyber security relies on the availability of insurance capital to mitigate economic security sector risks and support the reversibility of attacks. However, the cyber insurance industry is still in its infancy. Pressure on insurance capital from increasing natural disaster activity could consume the resources necessary for economic security in the cyber domain in the near term and create long-term conditions that increase the scarcity of capital to support cyber security risks. This article makes an original contribution by exploring the under-researched connection between the nexus of cyber and economic security and the climate change threat. Although the immediate pressure on economic resources for cyber security is limited, recent natural disaster activity has clearly shown that access to capital for cyber risks could come under significant pressure in the future.

Some fishing vessels breach maritime laws by operating with their mandatory tracking systems (Automatic Identification System (AIS)) switched off. Marine insurers act as enablers of this practice since these vessels cannot operate without insurance. This article explores why insurers in England take on the risk of insuring them and assesses how the insurers are operating against the regulatory framework in doing so. It identifies the solutions that could raise standards in marine insurance and lead to increased legal compliance by the insured vessels. This would consequently enhance maritime safety, while increasing transparency in fisheries across all oceans. Importantly, by discouraging vessels from going dark, any illegal activities underlying the non-transmission of AIS data, such as human, drug or weapon trafficking, illegal fishing or sanctions evasion, would also be curbed.

In this paper, we present experimental evidence on the effect adverse selection has on coverage choices and pricing in corporate insurance markets. Two sets of experimental data, each generated by experiments utilizing a specific parameterization of a corporate insurance decision, are presented to gauge these effects. In the first, subject behavior conforms to a unique equilibrium in which high risk firms choose higher coverage and contracts are priced accordingly. Insurers act competitively and convergence to equilibrium behavior is marked. In the second set, there is little evidence that subject behavior is consistent with either of the two equilibrium outcomes supported by the experimental setting—pooling by fully insuring losses and pooling by self insuring.

We conduct a framed field experiment in rural Ethiopia to test the seminal hypothesis that insurance provision induces farmers to take greater, yet profitable, risks. Farmers participated in a game protocol in which they were asked to make a simple decision: whether or not to purchase fertilizer and if so, how many bags. The return to fertilizer was dependent on a stochastic weather draw made in each round of the game. In later rounds a random selection of farmers made this decision in the presence of a stylized weather-index insurance contract. Insurance was found to have some positive effect on fertilizer purchases. Purchases were also found to depend on the realization of the weather in the previous round. We explore the mechanisms of this relationship and find that it may be the result of both changes in wealth weather brings about, and changes in perceptions of the costs and benefits to fertilizer purchases.

We propose the use of Bayesian estimation of risk preferences of individuals for applications of behavioral welfare economics to evaluate observed choices that involve risk. Bayesian estimation provides more systematic control of the use of informative priors over inferences about risk preferences for each individual in a sample. We demonstrate that these methods make a difference to the rigorous normative evaluation of decisions in a case study of insurance purchases. We also show that hierarchical Bayesian methods can be used to infer welfare reliably and efficiently even with significantly reduced demands on the number of choices that each subject has to make. Finally, we illustrate the natural use of Bayesian methods in the adaptive evaluation of welfare.

Section 13A of the Insurance Act 2015 implies a term into every insurance contract that if the assured makes a claim under the contract, the insurer must pay any sums due in respect of the claim within a “reasonable time”. It provides contractual remedies, such as damages, in the event of breach. To date, a breach of section 13A has been pleaded in two cases which have resulted in judgments – Quadra Commodities S.A. v XL Insurance Co. S.E. and others [2022] EWHC 431 (Comm) and Delos Shipholding S.A. and others v Allianz Global Corporate and Specialty S.E. and others (“Win Win”) [2024] EWHC 719 (Comm) – and in each case the section 13A claim failed. Informed by the understanding of section 13A adopted in these two cases, this paper suggests that section 13A largely is symbolic. To this end, it considers three points and argues that two of these hinder the efficacy of section 13A.

This paper extends previous research on using quantum computers for risk management to a substantial, real-world challenge: constructing a quantum internal model for a medium-sized insurance company. Leveraging the author’s extensive experience as the former Head of Internal Model at a prominent UK insurer, we closely examine the practical bottlenecks in developing and maintaining quantum internal models. Our work seeks to determine whether a quadratic speedup, through quantum amplitude estimation can be realised for problems at an industrial scale. It also builds on previous work that explores the application of quantum computing to the problem of asset liability management in an actuarial context. Finally, we identify both the obstacles and the potential opportunities that emerge from applying quantum computing to the field of insurance risk management.

The theme of the 2024 Business History Conference was “doing business in the public interest,” but what does it actually mean to “do business in the public interest?” This presidential address challenges the idea of shareholder primacy as the main purpose of business enterprises historically and examines various ways that business historians might approach the idea of businesses acting in a public interest. In particular, it analyzes instances in which corporations made a decision in the public interest without clear evidence that it would benefit their bottom line; cases where it would demonstrably hurt their bottom line to prioritize the public; corporations that made a decision allegedly in the public interest that actually turned out to be bad for the public interest; and corporations that made a decision that was bad for the public interest that also turned out to be bad for their own bottom line.

The (re)insurance industry is maturing in its ability to measure and quantify Cyber Risk. The risk and threat landscapes around cyber continue to evolve, in some cases rapidly. The threat actor environment can change, as well as the exposure base, depending on a variety of external factors such as political, economic and technological factors. The rapidly changing environment poses interesting challenges for the risk and capital actuaries across the market. The ability to accurately reflect all sources of material losses from cyber events is challenging for capital models and the validation exercise. Furthermore, having a robust enterprise risk management (ERM) framework supporting the business to evaluate Cyber Risk is an important consideration to give the board comfort that Cyber Risk is being effectively understood and managed by the business. This paper discusses Cyber Risk in relation to important risk and capital model topics that actuaries should be considering. It is challenging for the capital models to model this rapidly changing risk in a proportionate way that can be communicated to stakeholders. As model vendors continue to mature and update models, the validation of these models and the ultimate cyber capital allocation is even more complex. One’s view of risk could change rapidly from year to year, depending on the threat or exposure landscape as demonstrated by the ransomware trends in recent years. This paper has been prepared primarily with General Insurers in mind. However, the broader aspects of capital modelling, dependencies and ERM framework are relevant to all disciplines of the profession.