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 rise in interest rates globally in 2022–23 led to improved scheme funding for many defined-benefit pension schemes. Many schemes in the UK now find themselves closer to, or at, a fully funded position on a low-risk basis (annuity buyout or self-sufficiency). Finishing the journey while managing the risk of losses on that journey is highly desirable, but may be difficult to achieve in practice.

However many schemes are not yet sufficiently funded to buy out liabilities in full with an insurer. Others may not wish to, and many who can afford to do so are not yet able to for investment reasons (such as holding illiquid assets) or operational reasons (such as the time needed to resolve member data issues). For schemes that instead look to adopt self-sufficient ongoing management with low dependency on the sponsoring employer, this may be difficult to maintain in practice. In short, there remains a risk that benefits will not be secured in full, which with hindsight could have been avoided.

The addition of capital to pension scheme assets has long been deployed to enhance the security of member benefits e.g., capital from insurers in the case of a buyout or capital from sponsors in the form of contingent assets.

More recently, providers have developed a diverse set of arrangements that draw on external capital to aid trustees and corporates to meet scheme funding ambitions. Capital Backed Funding Arrangements (“CBFA”) are in this context an additional tool in the trustee toolkit for delivering funding strategies.

This paper focusses on the UK-defined benefit market but the dynamics are applicable to other jurisdictions, with CBFAs being developed for wider markets (e.g., Ireland).

In this paper we:

• survey the current scheme funding landscape and consider the need in this environment for arrangements to support scheme funding journeys to deliver benefits in full

• summarise the key features of arrangements in the market that may support these objectives

• set out considerations for trustees and sponsoring companies when assessing these arrangements.

The aim of this paper is educational – to increase awareness of the key issues and potential solutions. Professional advice will always be required prior to any transaction. We welcome feedback from readers on further material that would be beneficial to support consideration of these arrangements.

In the present technological age, where cyber-risk ranks alongside natural and man-made disasters and catastrophes – in terms of global economic loss – businesses and insurers alike are grappling with fundamental risk management issues concerning the quantification of cyber-risk, and the dilemma as to how best to mitigate this risk. To this end, the present research deals with data, analysis, and models with the aim of quantifying and understanding cyber-risk – often described as “holy grail” territory in the realm of cyber-insurance and IT security. Nonparametric severity models associated with cyber-related loss data – identified from several competing sources – and accompanying parametric large-loss components, are determined, and examined. Ultimately, in the context of analogous cyber-coverage, cyber-risk is quantified through various types and levels of risk adjustment for (pure-risk) increased limit factors, based on applications of actuarially founded aggregate loss models in the presence of various forms of correlation. By doing so, insight is gained into the nature and distribution of volatile severity risk, correlated aggregate loss, and associated pure-risk limit factors.

This report explores key considerations in relation to adopting a dynamic discount rate funding approach and the impacts of doing so in a range of areas, including funding volatility, investment strategy and end game objectives. It considers the advantages and disadvantages of this approach from the perspective of a range of stakeholders and the challenges that need overcoming in order to fully implement and support the approach, for example data challenges and the new skills required in the industry. The report includes sample modelling to highlight the practical issues that arise when adopting this approach. It describes a step-by-step approach for assessing the risks to be considered when determining an appropriate level of assets to provide funding for a sample set of pension scheme cash flows, as summarised in the table below.

It also considers how a dynamic discount rate approach fits within the proposed future funding regulations. Finally, the report puts forward recommendations for the IFoA, Scheme Actuaries and TPR.

Consequences of schemes adopting a dynamic discount rate approach could include very different investment strategies with investment in a wider pool of assets, less use of leveraged Liability Driven Investment, fewer schemes targeting buy-out as their end game strategy and an increase in technical work for actuaries in advising on the optimisation of asset and liability cash flows.

We all know the importance of taking advice from the right people. In the financial services arena, good quality financial advice helps millions of people manage their money, realise their goals and achieve long-term financial security. Given the low level of financial literacy in the general population, financial advice can be seen as a necessity for millions of people. However, our brief research to date indicates that a huge advice gap has emerged in the UK following the introduction of the Retail Distribution Review in 2012. And it doesn’t look like this will get better soon. Regarding this advice gap, the FCA stated in 2023 that “the provision of financial advice is often out of reach for all but the already wealthy.” Furthermore, efforts to mitigate the advice gap are either non-existent or piecemeal at best.

The consequences of the advice gap include poor investment decisions leading to sub-optimal savings: research shows that consumers in the UK who took professional financial advice between 2001 and 2006 enjoyed an average increase in their assets of nearly £48,000 (∼20%) after ten years, compared to those who took no advice. In this paper, we consider various mechanisms to improve access to financial advice and guidance and reduce the size of the advice gap while recognising that these should be implemented under the umbrella of Consumer Duty, which should ensure that customers receive good outcomes.

Finally, in terms of financial inclusion, there is a gender and social imbalance with women and those from lower socio-economic groups less likely to access formal advice or guidance. This is where the advice gap is biggest, and some of these groups of people could benefit most from access to financial advice or guidance, particularly those groups with lower levels of financial literacy who have reduced means to start off with. Implementing measures to reduce the advice gap under the umbrella of a strong consumer duty should provide customers with the means to pay for the level of advice that they need while being confident that they are receiving the right products for their needs at the right price leading to good customer outcomes.

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.

This paper uses a two-step approach to modelling the probability of a policyholder making an auto insurance claim. We perform clustering via Gaussian mixture models and cluster-specific binary regression models. We use telematics information along with traditional auto insurance information and find that the best model incorporates telematics, without the need for dimension reduction via principal components. We also utilise the probabilistic estimates from the mixture model to account for the uncertainty in the cluster assignments. The clustering process allows for the creation of driving profiles and offers a fairer method for policyholder segmentation than when clustering is not used. By fitting separate regression models to the observations from the respective clusters, we are able to offer differential pricing, which recognises that policyholders have different exposures to risk despite having similar covariate information, such as total miles driven. The approach outlined in this paper offers an explainable and interpretable model that can compete with black box models. Our comparisons are based on a synthesised telematics data set that was emulated from a real insurance data set.

The efficient integration of outpatient (OPD) and inpatient (IPD) care is a critical challenge in modern healthcare, essential for maximising patient-centred care and resource utilisation. OPD, encompassing preventive services, routine check-ups, and chronic disease management, aims to minimise the need for hospitalisations. Conversely, IPD remains crucial for acute interventions and complex medical needs. This paper investigates the nuanced relationship between OPD and IPD: specifically, we seek to determine if increased OPD utilisation while improving overall health, leads to a reduction or increase in subsequent IPD utilisation, duration, and associated costs. We analyse anonymised data from Indian organisations providing company-sponsored OPD and IPD insurance to their employees between 2021 and 2024. By examining the correlation between OPD utilisation patterns and IPD outcomes, we aim to provide data-driven insights for effective healthcare integration strategies. Furthermore, we explore the feasibility of developing a personalised “Wellbeing Rating” derived from longitudinal OPD insurance utilisation data. This automated methodology aims to provide a continuous, dynamic health assessment, moving beyond the limitations of traditional, sporadic medical examinations by leveraging the comprehensive data inherent within insured OPD coverage.