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The rapidly evolving landscape of insurance in the modern era brings with it a unique conundrum—how do we ensure fair pricing, especially when introducing new products with sparse data? Though long-standing, the principle of fairness and anti-discrimination is complicated by the dynamics of innovative products, evolving consumer behaviour, and the complexities of AI and emerging risks. In the first part of this presentation, Michael will share practical challenges he faced when helping relaunch the startup insurtech PassportCard Travel Insurance after the Covid pandemic forced the hibernation of the business. He will discuss the team's approach to applying established guidance and literature around anti-discrimination for protected attributes such as age and disability, especially where the apparent lack of data requires more diverse thinking. Through real-world examples, attendees will gain insight into practical strategies to navigate these challenges, ensuring product pricing that aligns with both business goals and societal expectations. In the second segment of our session, Fei will review the state of the art of research in this area with an international perspective. She will then respond to the practical challenges shared by Michael with insights and potential solutions. A list of open questions that need further work and collaboration between academia, government, and industry will be summarised and discussed in the end.

Traditional aggregate reserving techniques assume that each data triangle used is a homogeneous group of claims. However, the practical constraints of management and reporting requirements often hinder the adoption of a statistically optimal approach to segmentation. There is often a reliance on legacy class hierarchies, business processes or expert judgement due to the difficulty of determining an optimal segmentation. This can reduce homogeneity in triangles and affect the performance of aggregate reserving techniques. To address these challenges, we propose a framework and methodology for automated segmentation to allocate individual claims in a reserving class to homogeneous subgroupings more suitable for triangle-based projections. The proposed approach involves use of machine learning techniques to: • Evaluate claim features that can relate to claim development in a statistically significant way. • Implement an algorithm to segment claims. • Assess and compare different segmentations on their impact on claim reserve accuracy. We also explore the practicalities for constructing models in a way that allows managing segmentation changes across reporting periods. Overall, we propose a methodology and practical framework for leveraging clustering techniques as part of a reserving process to improve accuracy and reduce the need for intervention of actuaries managing insurers’ claims reserves.

In recent times, the reinsurance market has been going through a hardening phase with substantial price increases (corrections), tightening coverage, and even withdrawn capacity. While it is relatively well understood that the key drivers are post-COVID recoveries, challenging state of the economic, claims activities, and uncertainty associated with climate changes and geo-political risks, the solutions are not obvious, especially for the public sectors. When it comes to natural catastrophe risks, many in the private sector speaks of the language of economic and/or regulatory capital, e.g., ICRC, 1/200, 1/1000, return period, etc. The public sector speaks the language of funding, e.g., taxations, levy, etc. Since (re)insurance is an important form of funding for the government, a hardening (re)insurance market means that the government will face even wider protection gap, delay in infrastructure projects development, bigger risk in economic development, etc. Thera are, however, other public sector solutions available which has proven to be innovative and effective. These solutions help facilitate economic development and increase insurance protection and financial inclusion. In this context, the public sector refers to governments (at sovereign and sub-sovereign levels), state-owned enterprises (SOEs), sovereign wealth funds (SWFs), development banks, export credit agencies (ECAs), multilateral organisations and 'third sector' entities/NGOs. The presentation will conclude with a few case studies. Indicatively, there will be case studies like: (To be confirmed) - Catastrophe bond for Pacific Alliance sovereign earthquake protection covering four countries. - Mexico Hurricane Coral Reef Cover - first insurance coverage to protect a natural asset. - Sovereign catastrophe risk transfer in the Asia-Pacific region – PCRIC - Livestock protection against drought for smallholder farmers - Longevity swap for sovereign pension

FY 2022/23 marked Australia’s cyber wake-up call – Australia experienced the largest breaches in its history, with the personal details of more than 10 million Australians impacted by one data breach alone. In response, there has been a suite of initiatives from government looking to tighten Australia’s cyber security settings, and prepare individuals, corporates and the companies for the breaches that will inevitability come. This has included strong signals from Australia’s corporate regulator that it will look to prosecute those companies who have not adequately prepared for cyber incidents, the development of a new cyber security strategy for the nation, and incorporation of cyber into new prudential standards for APRA regulated entities. Corporate Australia has responded to the ‘annus horribilis’ by increasing its investment in information security, and looked to the cyber insurance market to transform some of the risks faced. In this paper we: • Look at the major changes to the cyber landscape that have occurred over the previous year, and score how well corporate Australia has responded to the cyber wake-up call • Discuss how actuaries can use our unique skillset to address the challenges posed by cyber risk, and add value both in the boardroom and to policy makers by 1) measuring cyber risk, 2) designing solutions to address cyber risk, and 3) quantifying the impacts of cyber risk.

In this paper the author(s) address the effects of shifts in inflation on the work of general insurance actuaries in Australia with reference to: • The types of inflation (economic and superimposed, which are defined in the paper); • How the different types of inflation can affect: o Claim sizes over time; o Pricing o Reserving o Policy design o Asset liability management o Reinsurance • The allowance for inflation in models used by actuaries in general insurance work in Australia; There is also a discussion of the difficulties encountered in recent years in forecasting inflation. Earlier work in the area of inflation and its effects on general insurance in Australia by other Australian actuaries is discussed and acknowledged and used as a foundation for suggestions about further developments in actuarial practice in general insurance.

Artificial Intelligence is having a significant impact on the insurance industry, with rapid development and investment across the value chain. While AI presents opportunities for efficiency and improved service, it also brings new risks and regulatory challenges. We will explore three perspectives on the emerging relation between AI and insurance, with a focus on how regulatory and legislative changes are likely to impact insurers’ own use of AI, change the risk landscape for existing lines and introduce opportunities for new product innovations. We’ll cover: 1. Impacts of emerging regulations and standards on insurance applications: We'll explore the rapidly expanding legislative, regulatory and standards environment. Examples include specific AI legislation in the EU and US, emerging regulations in Australia and updates to existing legislation to explicitly capture algorithmic and AI risks, such as the proposed changes to the Privacy Act 1988 (CMW). Complementing the regulatory development are standards and guidelines on appropriate application and governance of AI, including the ISO 42 group of standards that seek to provide a rigorous framework for system validation and audit, akin to existing standards around Cyber risk. 2. AI-induced risks on existing insurance lines: Here we’ll address the ripple effects of AI on established lines. Examples include the potential for breach of duty claims impacting Directors and Officers insurance, complex changes to liability risk for organisations that include AI in their products and increased risks of privacy and data breaches impacting risk exposures for cyber cover. We'll discuss relevant international developments and examples, including the EU's ambitious Artificial Intelligence Liability Directive, which seeks to update the EU liability framework to make it easier for individuals to bring claims for harms caused by AI. 3. Insurance solutions for emerging AI risks: In this section, we'll cover the potential for insurance products that cover new and emerging AI risks, including existing emerging examples. This includes nascent product-warranty like cover, and cover for consequential losses arising from underperformance of AI models. We'll discuss potential for cover against insurability criteria, and some of the associated likely product, underwriting and claims challenges.

Over the past 20 years, the ageing population has seen an increase in the use of healthcare services. At the same time, costs per service have increased. These factors have driven private health insurers to increase premiums at a faster rate than wage inflation. At face value, this suggests that the affordability of private health insurance has declined over that time and has become less attractive to healthier people who are less likely to benefit from private health insurance. As healthy people lapse their cover, average benefit costs increase leading to higher premiums again, further conflating affordability issues leaving the private health insurance industry in an unsustainable position. While the COVID-19 pandemic saw an increase in participation amongst the young as well as low benefit inflation, these impacts are unlikely to be permanent. This paper seeks to explore what the Australian Private Health Insurance industry might look like under the status quo in another 20 years, and seeks to answer the following: • How will the ageing population continue to affect benefit levels? • How will affordability pressures impact participation, and in turn, insurance premiums? • What impact will Government incentives like the Medicare Levy Surcharge and the Private Health Insurance Rebate have? • Are certain private health insurers likely to fare better over the next 20 years than others? In exploring these issues, the paper aims to provide private health insurers with an understanding of the key risks to the long-term sustainability of their funds and actions that managers can implement in the short-tomedium term to improve sustainability.

Natural disasters are often linked to extensive insurance losses. Their occurrence is rooted in the prevailing climate conditions that facilitate weather extremes, causing socio-economic impacts and damages. Climate conditions, most notably conditions that promote weather extremes, can be expressed as so called “climate indices” – quantities that measure aspects of the planet’s circulation and weather dynamics. Climate indices have broad applications across many fields, first emerging from the scientific community to identify environmental trends in weather dynamics. Due to their versatility and relative simplicity, various sectors have since adopted climate indices into their operations (e.g., emergency services, agribusiness, and financial services). In the context of insurance, climate indices can provide a critical source of information for insurers in quantifying the broadscale geographic risk of natural hazards, and how these are changing with time. Further, climate indices are useful in quantifying the changing behaviour of extreme weather risks – being able to discriminate geographical differences, their frequency of occurrence, and to some extent their intensity. While traditionally the goal of insurance pricing is to set rates against the average cost of annualised losses, this approach largely ignores changing climate conditions caused by anthropogenic greenhouse gas emissions. This can lead to a misunderstanding of the role climate change and natural variability has on the interannual volatility of claim size and frequency, as the focus can be biased towards recent experience. To prevent this, Suncorp has developed a suite of climate indices based on observational data, output from climate models and the latest research from the scientific community. We use these indices to inform our view of developing climate risk – specifically, the natural hazard events which cause the largest insurance losses, i.e., flood, bushfires, tropical cyclones, hail, and storms. In this session, we demonstrate this practice and how we are using AI to expand the utility of climate indices in quantifying expectations on weather extreme claim size and frequency.

How can Australia create a much-needed housing stock that can be protected now and into the future, using affordable and relevant insurance? Actuary of the Year, Sharanjit Paddam, brings together diverse perspectives to find major solutions for improved living. We'll hear from Andrew Hall, CEO of the Insurance Council; Major-General (Retired) Jake Ellwood, CEO of the Queensland Reconstruction Authority; Jane Kern, Head of Impact Management, Bank Australia; and Matt Collins, CEO of the Planning Institute.

Artificial Intelligence is having a significant impact on the insurance industry, with rapid development and investment across the value chain. While AI presents opportunities for efficiency and improved service, it also brings new risks and regulatory challenges. We will explore three perspectives on the emerging relation between AI and insurance, with a focus on how regulatory and legislative changes are likely to impact insurers’ own use of AI, change the risk landscape for existing lines and introduce opportunities for new product innovations. We’ll cover: 1. Impacts of emerging regulations and standards on insurance applications: We'll explore the rapidly expanding legislative, regulatory and standards environment. Examples include specific AI legislation in the EU and US, emerging regulations in Australia and updates to existing legislation to explicitly capture algorithmic and AI risks, such as the proposed changes to the Privacy Act 1988 (CMW). Complementing the regulatory development are standards and guidelines on appropriate application and governance of AI, including the ISO 42 group of standards that seek to provide a rigorous framework for system validation and audit, akin to existing standards around Cyber risk. 2. AI-induced risks on existing insurance lines: Here we’ll address the ripple effects of AI on established lines. Examples include the potential for breach of duty claims impacting Directors and Officers insurance, complex changes to liability risk for organisations that include AI in their products and increased risks of privacy and data breaches impacting risk exposures for cyber cover. We'll discuss relevant international developments and examples, including the EU's ambitious Artificial Intelligence Liability Directive, which seeks to update the EU liability framework to make it easier for individuals to bring claims for harms caused by AI. 3. Insurance solutions for emerging AI risks: In this section, we'll cover the potential for insurance products that cover new and emerging AI risks, including existing emerging examples. This includes nascent product-warranty like cover, and cover for consequential losses arising from underperformance of AI models. We'll discuss potential for cover against insurability criteria, and some of the associated likely product, underwriting and claims challenges.

The compounding effects of heavy rainfalls and strong winds can lead to severe disasters, resulting in catastrophic losses. This paper presents a novel approach to modeling joint extreme climate events involving precipitation and wind speed. A case study is undertaken, utilizing data gathered from weather observatories spanning across New South Wales, Australia. In terms of methodology, we propose an automated threshold selection method and implement an automated declustering technique to address inherent challenges in applying extreme value theory to a substantial number of datasets. Remarkably, these methodological advancements are applicable to various other forms of joint climate extremes. Furthermore, we introduce a proxy derived from these joint extremes and create a heatmap to identify any significant trends of joint heavy rainfalls and strong winds. This proxy offers a refined understanding of the compounding effects of joint extremes and holds the potential to provide valuable insights for pricing insurance products.

Our profession's move into sophisticated data science was led by GI practitioners taking techniques developed/implemented for pricing to much wider areas and applications. Yet GI claims liability estimates are largely made using techniques and processes that were developed in the 1980s. At the time, spreadsheets were a new thing and appeared to be, and indeed were by the standards of the day, excellent tools for implementing the techniques. This situation no longer pertains. We argue that modern statistical and machine learning techniques, combined with well documented research specific to the use case, require the profession to move on from traditional approaches. The many benefits can include: • more statistically robust and accurate estimates; • more flexibility and efficiency of model specification (and variation/exploration thereof); • greater automation of and statistical basis for parameter estimation ; • separation of data from model logic; • better process control and documentation; • improved collaboration. We also discuss common arguments against moving away from spreadsheet-based artisanal processes, such as their flexibility, accessibility to non-coders and perceived adequacy. In addition to outlining the need for, and value of, wholesale change in the profession's approach to claims reserving, we explore what changes are necessary to professional standards, guidance and the education system in order to assist practitioners make this change. The work is based on the authors’ experience in designing, building, operating and reviewing reserving processes, as well as interviews with a number of senior reserving actuaries.

Price optimisation in insurance seeks to modify premiums away from risk cost in ways that maximise business objectives, such as increasing profit while achieving a target volume. Knowledge of customers’ price sensitivities can lead to sophisticated approaches to optimise premiums. While the practice has attracted some debate, it is generally accepted as effective. However, significant uncertainties and complexities arise on more careful analysis. Issues with model inaccuracy and complexities with multi-year impacts mean that establishing superior performance is less clear. This paper builds on existing research by exploring the situations where optimisation approaches can potentially struggle: • How do strategies play out over multiple years, and what is the impact on a portfolio’s price sensitivity over time? • How do nonlinearities in modelled demand curves, such as those induced by link functions, affect optimisation? • What are the practical impacts of model uncertainty on performance? • How do strategies fare in situations where customer price sensitivity can vary year to year? • How important is random price testing to maintaining accurate demand models? The paper will look at theoretical and simulated results, with an emphasis on understanding the robustness of standard optimisation approaches under plausible uncertainty assumptions on a multi-year basis.

The Federal Government’s Strengthening Medicare Report sets out an expansive agenda to improve healthcare in Australia through blended care pathways, supported by investment in better use of digital technology to share patient data. But the My Health Record system that underpins part of this plan has existed for over 10 years, has seen $2bn in investment and is still considered, to date, unsuccessful. This paper explores the reasons for those failures but also the possibilities for health and health actuaries should further investment in this system prompt the progress and growth long desired. With the Strengthening Medicare Report also putting the role of private health insurers in primary care back on the agenda, the presentation will also explore the current landscape and sentiments around private health insurers’ involvement in this area and the benefits to consumers of an appropriately safeguarded but better integrated data sharing platform.

We traditionally quantify climate change risks as a function of the risk determinants of hazard, exposure, and vulnerability. Traditionally, insurers have been concerned with the built environment domain, focussing on assessing the damage to properties and buildings from natural disasters. Many practitioners are extending climate risk assessments to other domains to look at climate risk more holistically, as climate change does not only impact the built environment, but also the social, natural, and economics domains. But there are limitations to only relying on a quantitative assessment as climate change risk is extremely complex and interacts both internally between the determinants of risk, and externally with many other risks. This can lead to cascading and compound events with widespread systemic risk impacts. For example, vulnerabilities in one domain can stem from multiple hazards, leading to exposures in other domains, whilst exposures that change over time can create changing vulnerabilities across domains. Hazard interactions from disasters may cause feedback loops that lead to untenable capacity to support social, economic, natural and built domains. A qualitative risk assessment can help identify deficiencies in quantitative modelling and suggest improvements for future risk assessments. Our methodology for qualitative assessment refers to reports aggregated from community interviews, which involves decision-makers, local officers, emergency response officers, and other stakeholders. The methodology combs through the main conclusions from the reports to form our basis for the interactions between hazard, vulnerability, and exposure under the four domains (built, social, economic, and natural). The confidence of the interaction increases where there are multiple instances of the same interaction being noted across different reports, or sometimes across similar themes within the same report. The output from our qualitative assessment are multiple mapping diagrams that show the interactions captured within the reports within and between domains. We then draw conclusions from the mapping diagrams, which can be then used to supplement the quantitative risk assessment and identify gaps for future assessments.

Pricing promises are representations made by insurers to help customers save money when purchasing insurance policies, provided they meet certain eligibility requirements. Over the past few years, developments have prompted general insurers across Australia to undergo reform and transformation due to failures in delivering pricing promises. This was largely initiated by ASIC, which required several insurers across Australia to conduct a review of their pricing practices. This primarily involves identifying instances where pricing promises were not fully delivered, addressing the root causes, and compensating affected customers. We will explore together: • How these pricing promise developments initially began • The launch of the ASIC pricing promises review • The current state of the industry from a pricing promises perspective • Strategies for enhancing pricing practices in the future

In an era where data reigns supreme, data science actuaries aren't just crunching numbers—they're shaping futures! Data Science Applications, as an actuarial Fellowship subject and microcredential, delves deep into modern data science techniques. The subject offers actuaries powerful tools to analyse, predict, and pioneer in this age of information. But how have these tools been implemented in the trenches of traditional actuarial work? By engaging with a panel of former students, this session will unpack the tangible benefits that data science brings to the actuarial table.

Over the past 20 years, the ageing population has seen an increase in the use of healthcare services. At the same time, costs per service have increased. These factors have driven private health insurers to increase premiums at a faster rate than wage inflation. This suggests that the affordability of private health insurance has declined over that time and has become less attractive to healthier people who are less likely to benefit from private health insurance. If healthy people lapse their cover, average benefit costs would increase leading to higher premiums again, further conflating affordability issues leaving the private health insurance industry in an unsustainable position which some researchers have termed a “death spiral”. In this paper we find that a death spiral is unlikely to emerge over the next 20 years even when assuming pessimistic assumptions around participation and cost inflation. We find that there are complex movements underpinning participation and benefit cost inflation which mitigate the risk of a death spiral, including: • Recent increases in youth participation. • Benefit utilisation which showed signs of slowing down even prior to the COVID-19 pandemic, and has persisted at lower levels well after the direct impacts of the pandemic on access to private hospital services. • There are cohort effects on participation, which results in a smaller impact of ageing on average benefits going forward. • Movements in prices do not appear to have a strong an impact on participation.