Given the significant financial risk involved in home building, it is critical for societal resilience that Home Building Compensation schemes remain efficient and effective. We set out six key areas we encourage the Review to consider, including: an increased maximum cover to reflect contemporary building costs; reviewing the maximum cover for incomplete work; and protection for ‘no fault’ owners who find themselves uncovered.

The number of Australian households facing home insurance affordability pressure has increased by 30% to 1.6 million in the past year due to rising premiums outpacing household income growth. In "Home Insurance Affordability and Home Loans at Risk", we update the Australian Actuaries Home Insurance Affordability Index and explore the factors driving this trend and its potential impact on the home loan market.

The Institute supports APRA’s ongoing role in the collection and reporting of industry data, the evolutionary approach it proposes to take and, more generally, the continuing development of insurance industry statistics. However, we encourage APRA to release additional information in general and life insurance beyond what is proposed to ensure its statistical publications support all stakeholders being well informed and provide further commentary regarding the proposals for private health insurance statistical publications.

The Institute supports the abolition of the Emergency Services Levy (ESL) and its replacement with a more equitable source of revenue collection. Reform of the ESL and replacement of other insurance taxes has been called for by the Institute for some years, as one of the most immediate steps that can be taken to address home insurance affordability and availability in NSW, and thereby improve societal resilience.

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: 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. 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. 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.

In 2022 economic inflation increased to levels not seen in Australia for the last 25 years. In this paper the authors address the effects of this shift in inflation on the work of general insurance actuaries in Australia, and examine the theory and evidence of its impact on:  Claims;  Premiums;  Insurance margin;  Investment returns; and  Capital. Earlier work on inflation and its effects on general insurance in Australia by other Australian actuaries is discussed and acknowledged and used as a foundation. Comparisons with experience and thinking overseas are reflected upon. The final section of the report contains a discussion of the difficulties encountered in recent years in forecasting inflation and what this means for the uncertainty in forecasts in future. The purpose of this paper is to generate a discussion at the Summit of the techniques available to the actuary for addressing risks associated with inflation, especially changing inflation and superimposed inflation.

Natural catastrophes can cause large losses for re/insurers which can amount to a significant proportion of the local economy. The impact of demand surge (interchangeably referred to as post/catastrophe event loss amplification/PLA), primarily driven by increasing cost of repair or replacement of damaged property that may occur following a large-scale event when many individuals and organisations compete for limited supply of labour and materials needed for repair, should not be overlooked in evaluating total event losses. There are limited literature and publications to date on the topic of modelling demand surge on a quantitative basis. Among these the focus is either on one major driver or several minor drivers that doesn’t provide a holistic and straightforward solution that can be adapted for insurance loss modelling. Although major catastrophe modelling companies such as Verisk (previously known as AIR) and RMS demonstrate sophistication in considering PLA in modelled losses, their respective approaches to PLA does not always equal to that observed in historical events. This can be attributed to factors including generalisation that overlook PLA effect at regional level and economic cyclones with which events coincided. This research reviews important drivers of PLA and derives from past events a more universal relationship between PLA and economic measures accessible by insurers that can be applied across different countries and regions. Key findings can be summarised as: a) PLA is related to size of loss as a percentage of size of the economy immediately impacted; b) Countries with large railway and road transportation network and larger population will suffer less disruption in supply chain post disaster, therefore meaning that disaster impacted regions have better access to the rest of economy and labour market in the nation; c) remoteness of impacted region is an important driver of PLA. The paper is an extended study of economic impact from climate risk. It also includes technical experiment on Generalized Additive Modelling of Sample Extremes, as well as qualitative discussions on alternative economic model, including Input-Output Model (I-O), Computable General Equilibrium (CGE) modelling and Integrated Assessment Models (IAM).

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.

There is a growing need to review approaches to insurance pricing such that they continue to serve the public interest. This need for a review of insurance pricing principles has been highlighted by three overarching themes. Firstly, community expectations, which are a function of social factors and society, evolve over time. The community has always expected high standards of conduct from financial institutions including insurers, and increasingly expects institutions to explicitly consider the perspectives of an increasing number of distinct and different customer groups. Community expectations regarding the pricing of insurance products, particularly around transparency and affordability, are topical, with an increasing onus on companies to explain and justify insurance pricing for different customer groups or individual customers. At the same time, inflation and other factors are driving up claim costs, and insurers must consider this in pricing products if they are to remain financially sound to pay claims when they are called upon. Insurers alone may not be able to fully meet the expectations of every stakeholder group in all situations. There are tensions, limitations and trade-offs involved with needs of different stakeholders which need to be considered, which we discuss in this paper. Secondly, recent regulatory interventions in the insurance industry have highlighted issues in established pricing practices, which have not met customer and community expectations. Regulators in Australia and overseas have introduced stricter requirements for insurers to safeguard consumers’ interests such as the introduction of product design and distribution obligations in Australia1 and the consumer duty obligations in the UK2 . Regulators have conducted reviews into insurers’ pricing practices which uncovered several shortcomings in how insurers met their pricing obligations towards customers3 . Thirdly, the rapid advancement of technology and data capabilities has led to more complex data processes, models and additional considerations which have introduced layers of complexity into pricing decision-making processes. These technology and data advancements could disrupt the balance among the diverse objectives of different stakeholders. Pricing decisions now carry a greater level of intricacy, requiring both risks and controls to be carefully reviewed. In this context, insurance pricing and its feedback loop becomes more crucial than ever to align pricing approaches with evolving values of transparency, fairness, and a customercentric focus. This alignment will consider the varied interests of stakeholders, reflecting a collective commitment to fairness and balance. This Research Paper has been written to provide a thought stimulus for professionals working in insurance, including actuaries, with responsibility for and oversight of pricing-related functions. It discusses six principles we suggest be considered in a pricing process. The six principles are not intended to serve as the definitive list that must be considered, although it is important that each business has a set of principles that are well articulated, understood, applied and monitored to ensure there is strong governance. Each business situation is unique, and professionals should continue to use their best judgment regarding the relevance of the principles presented in this paper to their situation and the extent to which other factors exist.

We model the relation between excess returns, fund size and industry size for active equity funds within four markets – global equities, emerging markets, Australia core and Australia small caps – and use the results to investigate the extent to which funds deviate from estimated capacity. We uncover a significantly negative relation between returns and both fund size and industry size across all markets. The estimated percentage of funds operating above versus below capacity varies both across markets and over time, and in the role played by fund size versus industry size. We find greater prevalence of funds operating significantly below than above capacity, in contrast to findings for US equity mutual funds. Significant deviations from estimated capacity persist for a median of between two and six quarters. Our main contribution is to show that the dynamics governing deviations from capacity for active equity funds vary across markets.

The Institute agrees that the introduction of standard definitions has merit and be limited to terms that cause or are expected to cause significant difficulty due to inconsistency. Standard terms can help with the availability of some, but not all, insurance at an affordable price. The Institute supports Option 2 and encourages the Government to consider a variation of it.

Reported autism prevalence has risen markedly over the last decade and is rapidly becoming an area of focus for many countries around the world. In Australia, the National Disability Insurance Scheme (NDIS, Scheme) is the primary means of government financial support available for people with disability. NDIS participants with autism and developmental delay, which can often precede an autism diagnosis, account for 45% of all participants and 75% of participants under 18 years of age. Understanding why the largest cohort in the Scheme is rapidly increasing can provide insight to inform effective Scheme design. This paper summarises key findings from recent research into international trends of autism prevalence and the proposed drivers of these increasing trends. The paper also includes quantitative analysis using the Person-Level Integrated Data Asset (PLIDA, formerly MADIP) from the Australian Bureau of Statistics to assess the potential impact of changing government financial incentives on autism prevalence in Australia using the roll out of the NDIS by age and region as a natural experiment.

XAI (Explainable Artificial Intelligence) is a research area focused on developing AI systems that can provide explanations for their decisions and actions. The goal of XAI is to make AI more transparent, interpretable, and trustworthy for end-users. The research in XAI involves developing new techniques and methods to generate explanations for AI models and designing interfaces that allow users to interact with the AI systems and understand the explanations provided. Well-Known machine learning method such as XGBoost predictive classification algorithm, neural network, support vector machine etc. have been broadly accepted to be helpful in insurance claims management in academic context. However, due to the limit in complexity and explainability, there is still a long way to go if we want to use those methods in the industry environment. XAI methods provide a gateway for industry users to open the black box and understand how the underlying advanced machine learning method behaves, and why the machine learning algorithms provides such results. Given there are plenty of existing XAI application in claims management, this paper will focus on discussing how XAI can be helpful in injuries and disability claims data. Furthermore, this paper will provide a feasible evaluation framework to do an industry level of user-based evaluation of the effectiveness of explanations provided by XAI system. This evaluation framework allows us to improve the trust to the XAI system, and ground the advanced mechanism to the reality.

Governments are taking steps to make amends for historical practices which are now recognised to have caused significant harm to those involved. These steps can be seen in royal commissions, parliamentary hearings and inquiries into issues such as governmental policies relating to First Nation Australians and the abuse of children in care. As governments make apologies for these historical injustices, they have also sought to provide justice to those harmed by these practices or failings in the form of redress or reparation schemes. These schemes acknowledge wrongs that have occurred in the past and provide affected individuals with financial payments, recognition, acknowledgement, trauma-informed counselling and other support services, as well as avenues for a direct apology from responsible institutions. This paper will provide an overview of the various redress and reparation schemes around Australia. It will explore how actuarial work in these schemes differs from typical accident compensation schemes and outline the various unique factors for consideration.

This Report explores the context of all natural perils and discovered that the proportion of “affordability stressed” households – those that spend more than one month’s worth of their gross annual income on home insurance – rose from 10 to 12 per cent. These households spend on average 8.8 weeks of their income on home insurance, which is more than seven times what the average household spends.

This Report explores the context of all natural perils and discovered that the proportion of “affordability stressed” households – those that spend more than one month’s worth of their gross annual income on home insurance – rose from 10 to 12 per cent. These households spend on average 8.8 weeks of their income on home insurance, which is more than seven times what the average household spends.

Funding for Flood Costs: Affordability, Availability and Public Policy Options

Developed with the Australian Human Rights Commission, this resource provides guidance on complying with federal anti-discrimination legislation in relation to use of artificial intelligence in insurance pricing and underwriting decisions.