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The research aims to describe the state of enterprise risk management (ERM) in the insurance sector. It highlights emerging trends in the application of risk management in the insurance sector and thereby reports the prominent research gaps and new avenues for research in ERM. The research adopts a systematic literature review (SLR) approach, using 187 research papers spanning 44 years (1977–2021). The paper identifies the fact that most ERM and insurance sector research is performed in North America and Europe, while developing economies in Asia and Africa lag. The paper establishes a three-way relationship between ERM, risk management (RM) and risk-based capital (RBC) where RM is a subset of ERM and RBC is a driver of ERM. The research shows that very few studies are conducted on risk culture, three lines of defence and the role of chief risk officers. The determinants of ERM identified are board, firm size, audit and risk management committee and corporate governance. The determinants identified for firm value are return on assets, return on equity, profit, Tobin's Q, among others. This research provides a way for academicians, practitioners and policy makers to design effective strategies for implementing ERM in organisations. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a comprehensive framework for discussing sustainability within the insurance industry, extending the traditional Environmental, Social, and Governance (ESG) dimensions to include economic and technological considerations. This inclusion is vital, recognizing that financial stability and the adoption of innovative technologies are fundamental to meeting other sustainability targets. Design/methodology/approach: We base our findings on an extensive literature review, case studies, and interactive workshops with key stakeholders in the insurance industry. Our analytical framework employs Porter's (1985) insurance-specific value chain, complemented by Berliner's (1982) insurability criteria, to distinguish between insurable and non-insurable risks. Findings: Our results show that the insurance industry is sustainable because it actively incorporates and contributes to sustainability goals across environmental, social, economic, and technological dimensions. This is illustrated through the identification of 50 distinct contributions across the insurance value chain, showcasing the sector's unique position to significantly influence the sustainability discourse. Practical implications: Addressing the pressing challenges of sustainability and insurability necessitates a strategic, collective response from the global insurance and risk management community. This paper proposes several policy recommendations, including enhancing risk assessment methodologies, diversifying insurance product offerings, encouraging cross-sectoral collaboration, and prioritizing investments in resilience and preventive measures. Originality/value: By broadening the sustainability discussion to encompass economic and technological facets, this paper enriches the dialogue surrounding the insurance industry's role in sustainability. It aims to inform decision-makers across the industry, political spheres, and broader society about the necessity of sustainability, fostering pertinent political discussions and highlighting avenues for future research. [ABSTRACT FROM AUTHOR]

The article discusses the 50th Seminar of the European Group of Risk and Insurance Economists (EGRIE) and the special issue of the Geneva Risk & Insurance Review (GRIR) that was published to celebrate this milestone. The seminar featured presentations and discussions on various topics related to risk and insurance, including self-protection, adverse selection in insurance markets, evaluating sustainability actions under uncertainty, the impact of big data and AI on insurance markets, and methodological challenges in insurance experiments. The article highlights the contributions of EGRIE researchers and calls for further research in these areas. [Extracted from the article]

In the context of advancing the transformation and upgrading of grassland animal husbandry, encouraging and guiding the widespread adoption of livestock insurance plays an important role in promoting the sustainable development of the livestock industry. This paper explores the impact of herders' attitudes and perceptions towards climate change risks on their livestock insurance strategies. Firstly, experimental economics methods are employed to measure the risk preferences of herders on the Tibetan Plateau. Secondly, a theoretical model incorporating risk preferences and insurance adoption behavior is constructed. Finally, the effects of herders' risk preferences on insurance adoption behavior are empirically examined through double-hurdle models, instrumental variable models, and moderating effect models. The results reveal that (1) most herders on the Tibetan Plateau exhibit risk-averse characteristics. (2) The degree of risk preference has a significant negative impact on herders' insurance adoption behavior, while the risk perception significantly positively influences insurance adoption. The results remain valid even after addressing issues of endogeneity and conducting robustness checks. (3) Livestock income plays a crucial moderating role in the mechanism through which risk attitudes affect insurance adoption behavior. (4) The impact of risk preference on insurance adoption behavior shows regional and income heterogeneity. [ABSTRACT FROM AUTHOR]

Introduction/Main Objectives: This study explores the application of machine-learning techniques to risk-based premium calculations for insurance guarantee schemes within the Indonesian insurance market. This study aims to develop a risk-based premium calculation model using machine-learning techniques in the Indonesian context. Background Problems: A gap exists in determining risk-based premiums for both the life and non-life insurance sectors within the Indonesian insurance market. Identifying and understanding the key variables that significantly influence risk-based capital (RBC) is important, and this research addresses this need. Novelty: This paper is the first to apply machine learning to calculate risk-based premiums in the context of the Indonesian insurance market. The distinction between the life and non-life insurance sectors in terms of the importance of its variables and itsselection of an optimal model further enrich its unique approach. Research Methods: We employed gradient-boosted and decision-tree models to identify key factors impacting risk-based capital. Furthermore, we leveraged clustering techniques to categorize companies into distinct risk tiers, aiming to enable more precise risk-based premium rate calculations. Finding/Results: The findings reveal significant differences between the life and non-life insurance sectors in terms of key variables that impact their risk-based capital. These insights lead to the categorization of insurance companies into distinct risk tiers whichhelps to more accurately calculate risk-based premiums. Conclusion: Machine learning can serve as a powerful tool in refining insurance risk management practices, ultimately benefiting insurers, policyholders, and regulators alike. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to identify the publication phase, performance and scientific contributions of research papers related to enterprise risk management (ERM) and to visualise the emerging themes in addressing volatility, uncertainty, complexity and ambiguity (VUCA). Design/methodology/approach: The biblioshiny technique based on the bibliometrix R package was used to draw journal papers' performance and scientific contributions by displaying distinctive features from the bibliometric method used in prior studies. The data was extracted from the Web of Science (WOS) and Scopus databases. Findings: Since the 1990s, ERM publication has gained momentum, and it is generally categorised into four main themes. Studies by Miller (1992) and Bromiley et al. (2015) scored the highest in global and local citations, respectively. However, the Economic Outlook ranked first in quality of publications while the Journal of Risk and Insurance topped in quantity of publications. Collaborative research mainly exists between two authors, and the dynamic number of collaborative networks is evident in the USA. Research limitations/implications: This study is limited by the filtered keywords used to generate the search on journal papers' in WOS and Scopus. It is imperative to have more comprehensive and rigorous analytics on ERM research to enable a direction for future research. Finally, ERM implementation better equips firms to mitigate risk in a VUCA environment. Originality/value: This study attempts to fill a vacuum of ERM literature, specifically in business economics, in addressing VUCA. Moreover, it covers a comprehensive predetermined period of from its inception in 1983 until 2022. [ABSTRACT FROM AUTHOR]

The frequent occurrence of mega project accidents has created an issue of risk management and has made its solution highly valued. In the case that the owner is at a regulatory disadvantage, insurance institution can provide a new pattern for risk management of mega projects. The purpose of this paper is to study the impact of insurance institution's participation in mega project risk management on the decision-making of all participants and the promotion of the overall effectiveness of collaborative risk management of all participants. By constructing a tripartite evolutionary game model between the insurance institution, supervision unit and contractor, the conditions of the behavior evolution process of each participant and the important parameters affecting the change in behavior strategies are analyzed. The results indicate that the participation of insurance institution can promote the risk management investment of the supervision unit and contractor, and punitive measures can make participants pay more attention to the losses caused by the occurrence of risks In order to encourage insurance institution to participate in risk management of mega projects, incentive measures need to be taken to dispel their concerns cost surrounding costs. This study is helpful to reduce the probability of risk occurrence so as to realize the sustainable development of mega projects and provides management suggestions for insurance institution to participate in risk management. [ABSTRACT FROM AUTHOR]

In this paper, a class of reinsurance contracting problems is examined under a continuous-time principal–agent framework with mean-variance criteria, where a reinsurer and an insurer are assigned the roles of the principal and the agent, respectively. Both parties can manage their insurance risk by investing in a financial portfolio comprising a risk-free asset and a risky asset. It has been assumed that both the insurer and the reinsurer are concerned about model uncertainty and that they aim to find a robust reinsurance contract and robust investment strategies by maximizing their respective mean-variance cost functionals taking sets of probability scenarios into account. To articulate the time-inconsistency issue attributed to the mean-variance optimization criteria, the optimization procedure of each decision-maker has been formulated as a non-cooperative game and discussed by using an extended HJB equation, which is consistent with the extant work on time-consistent control. Moreover, explicit expressions for the robust reinsurance contract, the robust investment strategies and the value functions of the insurer and reinsurer have been obtained and presented. The numerical results and their economic interpretations are then discussed. [ABSTRACT FROM AUTHOR]

This paper showcases the effectiveness of the discrete generalized Burr-Hatke distribution in analyzing insurance claims data, specifically focusing on scenarios with over-dispersed and zero-inflated claims. Key contributions include presenting foundational statistical theories with mathematical proofs to enrich the paper's mathematical and statistical aspects. Through the application of this discrete distribution, the study conducted a thorough risk analysis across five diverse sets of insurance claims data, evaluating critical risk indicators at specified quantiles. These indicators provided detailed insights into potential losses across different risk levels, supporting effective risk management strategies. The research emphasizes the importance of selecting appropriate probability distributions when analyzing zero-inflated data, as commonly observed in insurance claims. The discrete distribution accommodated these unique data characteristics and facilitated a robust analysis of risk metrics, enhancing the accuracy of potential loss assessments and reducing associated uncertainties. Furthermore, the study highlights the practical relevance of the discrete distribution in addressing specific challenges inherent to insurance claims data. By leveraging this distribution, insurers and risk analysts can improve their risk modeling capabilities, leading to more informed decision-making and enhanced financial exposure management. [ABSTRACT FROM AUTHOR]

In this paper, we draw insights from a 15-year qualitative research program exploring catastrophe insurance and gaps in insurance protection as risks escalate within a world impacted by a climate emergency. We suggest that the ebb-and-flow of our research team's composition and activities through time was inextricably linked with our ability to have a sustained impact on such a large-scale societal issue. The essay situates itself within the research impact and team literature, narrates the trajectory of our research program and team development, and develops a framework for effectively managing impact-oriented qualitative research teams over time. Our framework illustrates key aspects of this process including team (re)forming, building team and individual stickability, performing, and managing team flux. We also present 10 practical takeaways for how these aspects can be managed effectively to produce long-term impact work to address grand challenges. [ABSTRACT FROM AUTHOR]

Self-protection is a costly activity that reduces the probability of an unfavorable outcome. Even the simplest model with a binary risk of loss and expected utility of final wealth produces interesting comparative statics that are by no means trivial. This article provides a selective survey of the economics of self-protection. It puts particular emphasis on the contributions made by members of the European Group of Risk and Insurance Economists and research published in the Geneva Risk and Insurance Review. The article provides a conceptual framework to catalog existing models of self-protection, discusses the tension between risk aversion and downside risk aversion, reveals the role of probability thresholds, surveys extensions to non-expected utility, and highlights the recent surge in two-period models. Ideas for future research directions are also developed. [ABSTRACT FROM AUTHOR]

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, the frequency and severity of cybersecurity incidents have prompted customers to seek out specialized insurance products. However, this has also presented insurers with operational challenges and increased costs. The assessment of risks for health systems and cyber–physical systems (CPS) necessitates a heightened degree of attention. The significant values of potential damages and claims request a solid insurance system, part of cyber-resilience. This research paper focuses on the emerging cyber insurance market that is currently in the process of standardizing and improving its risk analysis concerning the potential insured entity. Design/methodology/approach: The authors' approach involves a quantitative analysis utilizing a Likert-style questionnaire designed to survey cyber insurance professionals. The authors' aim is to identify the current methods used in gathering information from potential clients, as well as the manner in which this information is analyzed by the insurers. Additionally, the authors gather insights on potential improvements that could be made to this process. Findings: The study the authors elaborated it has a particularly important cyber and risk components for insurance area, because it addresses a "niche" area not yet proper addressed in specialized literature – cyber insurance. Cyber risk management approaches are not uniform at the international level, nor at the insurer level. Also, not all insurers can perform solid assessments, especially since their companies should first prove that they are fully compliant with international cyber security standards. Research limitations/implications: This research has concentrated on analyzing the current practices in terms of gathering information about the insured entity before issuing the cyber insurance policy, level of details concerning the cyber security posture of the insured entity and way such information should be analyzed in a standardized and useful manner. The novelty of this research resides in the analysis performed as detailed above and the proposals in terms of information gathered, depth of analysis and standardization of approach made. Future work on the topic can focus on the standardization process for analyzing cyber risk for insurance clients, to improve the proposal based also on historical elements and trends in the market. Thus, future research can further refine the standardization process to analyze in more depth the way this can be implemented and included in relevant legislation at the EU level. Practical implications: Proposed improvements include proposals in terms of the level of detail and the usefulness of an independent centralized approach for information gathering and analysis, especially given the re-insurance and brokerage activities. The authors also propose a common practical procedural approach in risk management, with the involvement of insurance companies and certification institutions of cyber security auditors. Originality/value: The study investigates the information gathered by insurers from potential clients of cyber insurance and the way this is analyzed and updated for issuance of the insurance policy. [ABSTRACT FROM AUTHOR]

In this paper, we extend the research on the effect of corporate social responsibility (CSR) on firm risk by analyzing the CSR–idiosyncratic risk nexus and how CSR can be integrated as insurance in a global risk management strategy. First, the causality between CSR and risk was tested. Second, copulas were estimated to strengthen the existing results on the structure of the dependence between the different dimensions of CSR activities and idiosyncratic risk levels. The empirical analysis was conducted on a sample of 254 European-listed firms over the 2018–2020 period. The main results showed a directional causality effect between CSR and idiosyncratic risk, and the dependences were modeled between CSR and realized idiosyncratic risk. This allows a better understanding of the risk implications of CSR for investors, corporate managers, and policy makers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple dynamic mortality model to fit and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of factors. These factors are identified as interest rates with a given maturity. Similarly, we assume that changes in mortality rates depend linearly on changes in a specific mortality rate, which we call the key mortality rate. One of the main advantages of this model is that it allows the development of an easy to implement methodology to measure longevity and mortality risks using simulation techniques. Particularly, we employ the model to calculate the Value-at-Risk and Conditional-Value-at-Risk of an insurance product testing the accuracy and robustness of our proposal using out-of-sample data from six different populations. [ABSTRACT FROM AUTHOR]

In this paper, we study a delayed-claim insurance risk model perturbed by diffusion with general investment returns, in which each main claim may induce a delayed claim. Assume that the main claim sizes follow a one-sided linear process with independent and identically distributed step sizes. Furthermore, we assume that the step sizes and the inter-arrival times form a sequence of independent and identically distributed random pairs, with each pair obeying a bivariate Sarmanov distribution, and so do the delayed claim sizes and corresponding delayed times. In the presence of heavy tails, asymptotic upper and lower bounds for ruin probabilities are obtained. Finally, in order to verify the accuracy of our results, we conduct numerical simulations by Crude Monte Carlo (CMC) method. [ABSTRACT FROM AUTHOR]

With the advent of the "Internet of Things" (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler's (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program's scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program. [ABSTRACT FROM AUTHOR]

Generally, insurance companies face challenges in determining the Capital Requirement (CR) which is essential for business continuity. This situation could worsen if substantial number and amount of claims are requested by policyholders due to losses from catastrophic events such as floods, tsunamis, and earthquakes. In this paper, we propose a structured model to determine the CR for non-life insurance companies through loss ratios. Firstly, dependence structure among the business lines is modelled using copulas from both Elliptical and Archimedean copulas family with loss ratios from four business lines as the risk factors. The loss ratios are derived from incurred claims and earned premiums data of Malaysia’s non-life insurance businesses, such as Fire insurance, Motor insurance, Marine, Aviation and Transport (MAT) insurance, and Miscellaneous insurance. Subsequently, a combination of popular risk measures such as Value-at-Risk (VaR) and TailValue-at-Risk (TVaR) with a Drawable Vine (D-Vine) copulas model known as hybrid model is developed to estimate insurance companies’ risk capital. Finally, a simulation of Monte Carlo is commanded by calibrating data with our selected model to project the CR. This study contributes to the literature by addressing the problem of determining the appropriate CR for non-life insurance companies, proposing an empirical model based on real non-life insurance company data with a hybrid model built from risk measures and D-Vine copula. [ABSTRACT FROM AUTHOR]

This paper is devoted to asymptotic analysis for a continuous-time risk model with the insurance surplus process and the log-price process of the investment driven by two dependent jump-diffusion processes. We take into account arbitrary dependence between the insurance claims and their corresponding investment return jumps caused by a sequence of systematic factors, whose arrival times constitute a renewal counting process. Under the framework of regular variation, we obtain a simple and unified asymptotic formula for the finite-time ruin probability as the initial wealth becomes large. It turns out that, in the weakly dependent case, the tails of the claims determine the exact decay rate of the finite-time ruin probability while the investment return jumps only contribute to the coefficient of the asymptotic formula; however, in the strongly dependent case, they both produce essential impacts on the finite-time ruin probability which is under-estimated in the weakly dependent case. [ABSTRACT FROM AUTHOR]

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler fourvariate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums. [ABSTRACT FROM AUTHOR]

Because of many firms' lack of collateral, banks have imposed loan limits as a form of risk-control regulations of bank financing. In response, loan insurance has emerged as a useful instrument to lift loan limits. To study the joint value of bank loans and loan insurance, this article investigates a supply chain composed of one supplier and one capital-constrained buyer who takes out a bank loan and potentially purchases loan insurance. The analysis reveals that both the supplier and the buyer can benefit from the bank financing with loan insurance in an insurance-cooperation region wherein the supplier is willing to reduce the wholesale price to entice the buyer to purchase insurance. Further still, in the insurance-cooperation region, worse contract terms of the bank loan benefit the supplier, whereas worse policy terms of loan insurance help the buyer. Such benefits can lead to a higher order quantity than the capital-abundant one and partially coordinate the entire supply chain. However, when production cost is low, the supplier and the buyer can encounter a Stackelberg prisoner's dilemma, in which both firms no longer cooperate with each other, and both are worse off. Nevertheless, a government subsidy for the insurance premium can not only reduce the Stackelberg prisoner's dilemma, but also improve the social welfare although it is not always optimal for the government to provide that subsidy. The main qualitative results are robust in a variety of extensions. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: The first author acknowledges support from the National Natural Science Foundation of China (NSFC) [Grant 72171162] and the Humanity and Social Science Youth Foundation of Ministry of Education of China [Grant 20YJC630148], and the second author acknowledges support from the NSFC [Grant 72232001]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4827. [ABSTRACT FROM AUTHOR]

This correction notice addresses an encoding mistake in an article titled "IT service outage cost: case study and implications for cyber insurance" published in The Geneva Papers on Risk & Insurance - Issues & Practice. The mistake involved the misrepresentation of certain symbols and letters in a diagram within the article. The original article has been corrected to rectify this error. The notice also includes a statement from the publisher, Springer Nature, emphasizing their neutrality regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

For some time now, Solvency II requires that insurance companies calculate minimum capital requirements to face the risk of insolvency, either in accordance with the Standard Formula or using a full or partial Internal Model. An Internal Model must be based on a market-consistent valuation of assets and liabilities at a 1-year time span, where a real-world probabilistic structure is used for the first year of projection. In this paper, we describe the major risks of a non-life insurance company, i.e. the non-life underwriting risk and market risk, and their interactions, focusing on the non-life premium risk, equity risk, and interest rate risk. This analysis is made using some well-known stochastic models in the financial-actuarial literature and practical insurance business, i.e. the Collective Risk Model for non-life premium risk, the Geometric Brownian Motion for equity risk, and a real-world version of the G2++ Model for interest rate risk, where parameters are calibrated on current and real market data. Finally, we illustrate a case study on a single-line and a multi-line insurance company in order to see how the risk drivers behave in both a stand-alone and an aggregate framework. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modeling of the time to cancelation must account for this specific behavior. Another stylized fact is that policies which are not canceled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. Moreover, frequentist point estimation based on Expectation–Maximization algorithm is also presented. An illustrative example emulates a typical behavior for life insurance contracts, and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components. In particular, the observed censoring in the insurance context might be up to $50\%$ of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme weather disasters under global climate change has greatly impacted agricultural production, caused crop yield and price fluctuations, affected the increase of farmers' income, and hindered the development of the rural economy. As a typical mountainous region in North China, Shandong Province is a major peanut-planting province. In recent years, the peanut planting area in Shandong Province has remained relatively stable at around 10 million hectares (approximately 6.67 million hectares), accounting for approximately 14% of the national peanut planting area. Affected by meteorological disaster risks and consequent price risks, peanut farmers in Shandong Province are faced with double threats of output and price. This study is aiming to analyze the income risks faced by peanut planting farmers in the northern regions of China and to develop a comprehensive understanding of the factors influencing their income variability. The study may investigate various aspects especially the market price fluctuations, socioeconomic factors affecting peanut farming and the correlation of the peanut pricing and the local insurance. The current policy-based peanut insurance makes it difficult to fully disperse risks and guarantee stable production with the permission of relevant government department. Therefore, based on the peanut yield and price data of Shandong Province from 1991 to 2017 and following the logical context of "meteorological disaster - yield risk - price risk - income loss - income insurance", this paper uses the Copula method to calculate the premium rate of peanut income insurance and finds that under the guaranteed level of 70%-90%, the pure premium rate is between 1.5%-4.15%. Compared with the current cost insurance with a flat rate of 4% in Shandong Province, the income insurance rate is generally lower than the current rate. Secondly, there are regional differences in income insurance rates. In addition, at a higher level of protection, the income insurance rate is higher. [ABSTRACT FROM AUTHOR]

This paper provides comparison results for general factor models with respect to the supermodular and directionally convex order. These results extend and strengthen previous ordering results from the literature concerning certain classes of mixture models as mixtures of multivariate normals, multivariate elliptic and exchangeable models to general factor models. For the main results, we first strengthen some known orthant ordering results for the multivariate ▪ -product of the specifications, which represents the copula of the factor model, to the stronger notion of the supermodular ordering. The stronger comparison results are based on classical rearrangement results and in particular are rendered possible by some involved constructions of transfers as arising from mass transfer theory. The ordering results for ▪ -products are then extended to factor models with general conditional dependencies. As a consequence of the ordering results, we derive worst case scenarios in relevant classes of factor models allowing, in particular, interesting applications to deriving sharp bounds in financial and insurance risk models. The results and methods of this paper are a further indication of the particular effectiveness of Sklar's copula notion. [ABSTRACT FROM AUTHOR]

We propose an approach to construct a new family of generalized Farlie–Gumbel–Morgenstern (GFGM) copulas that naturally scales to high dimensions. A GFGM copula can model moderate positive and negative dependence, cover different types of asymmetries, and admits exact expressions for many quantities of interest such as measures of association or risk measures in actuarial science or quantitative risk management. More importantly, this paper presents a new method to construct high-dimensional copulas based on mixtures of power functions and may be adapted to more general contexts to construct broader families of copulas. We construct a family of copulas through a stochastic representation based on multivariate Bernoulli distributions and Coxian-2 distributions. This paper will cover the construction of a GFGM copula and study its measures of multivariate association and dependence properties. We explain how to sample random vectors from the new family of copulas in high dimensions. Then, we study the bivariate case in detail and find that our construction leads to an asymmetric modified Huang–Kotz FGM copula. Finally, we study the exchangeable case and provide insights into the most negative dependence structure within this new class of high-dimensional copulas. [ABSTRACT FROM AUTHOR]

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two‐thirds of flood‐exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster‐scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take‐up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take‐up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short‐lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take‐up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non‐primary residences. Overall, these findings suggest that relying on households to self‐adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap. Plain Language Summary: Disaster‐scale floods are expected to become more frequent in a warming climate, impacting areas where insurance is not mandated. Many flood‐exposed properties are currently uninsured, potentially undermining the climate resilience of communities. We investigate whether households might adapt to the changing flood risk by purchasing and maintaining insurance over time. We find that while disaster‐scale floods increase insurance demand, this effect is temporary, even among counties experiencing consecutive disaster‐scale flood years. This effect is more salient among inland counties as well as among homeowners whose primary residence is exposed to a disaster‐scale flood. We conclude that relying on households to self‐adapt by maintaining insurance is insufficient for closing the insurance protection gap. Key Points: Increasing flood risk is impacting areas where flood insurance is not currently mandatedConsecutive disaster‐scale flood years increase insurance take‐up, but this effect diminishes over timeRelying on the autonomous adaptation of households will be insufficient for closing the insurance protection gap [ABSTRACT FROM AUTHOR]

Purpose: This study examined the influence of income and health insurance on the health-seeking behaviour of rural residents, addressing the concerns of endogeneity and heterogeneity bias. Design/methodology/approach: A two-stage residual inclusion was utilised to correct self-selection-based endogeneity problems arising from health insurance membership. Findings: This study provides support for Andersen's behavioural model (ABM). Income and health insurance positively stimulate rural residents' use of modern healthcare services, but the effect of insurance risks a downward bias if treated as exogenous. Further, the effect of health insurance differs between males and females and between adults and the elderly. Originality/value: This study advances the literature, arguing that, within the ABM framework, enabling (i.e. income and insurance) and predisposing factors (i.e. age and gender) complement each other in explaining rural residents' use of modern health services. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0223 [ABSTRACT FROM AUTHOR]

Tropical forest peoples rely on wild resources to cope with adverse shocks (i.e. natural insurance). However, research has shown its limited scope against health shocks that constrain labor supply responses. This article examines natural insurance against illness through the lens of gender. We conducted a large-scale household survey in the Peruvian Amazon, where men mostly do wild resource harvesting. We found that fishing and nontimber forest-product gathering increased against female illness and remittances increased against male illness, suggesting that the scope of natural insurance and risk sharing against illness is shaped by gender in a complementary way. (JEL O13, O15) [ABSTRACT FROM AUTHOR]

In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data. [ABSTRACT FROM AUTHOR]

Research on actuarial risk management practices (ARMP) and insurance firm performance has revealed inconsistent results. Therefore, a mediating factor such as innovation is needed to bridge between them. Studies exploring the relationship between ARMP and innovation have been largely qualitative. This study offered a quantitative model focusing on the mediating role of e-service innovation between ARMP and firm performance. The hypothesized relationships were tested using a structural equation model (SEM), with a sample from 98 Indonesian insurance companies and WarpPLS 7.0 as the analytical tool. The results indicated that ARMP significantly influenced e-service innovation but was insignificant for firm performance. Furthermore, the findings highlighted the significant role of e-service innovation in insurance firm performance, which implied that e-service innovation acts as a mediator in the relationship between ARMP and firm performance. The practical application of the research findings makes them directly relevant and beneficial to the insurance industry, especially in Indonesia. [ABSTRACT FROM AUTHOR]

Context. Analysis of data uncertainties in modeling and forecasting of actuarial processes is very important issue because it allows actuaries to efficiently construct mathematical models and minimize insurance risks considering different situations. Objective. The goal of the following research is to develop an approach that allows for predicting future insurance payments with prior minimization of possible statistical data uncertainty. Method. The proposed method allows for the implementation of algorithms for estimating the parameters of generalized linear models with the preliminary application to data of the optimal Kalman filter. The results demonstrated better forecast results and more adequate model structures. This approach was applied successfully to the simulation procedure of insurance data. For generating insurance dataset the next features of clients were used: age; sex; body mass index (applying normal distribution); number of children (between 0 and 5); smoker status; region (north, east, south, west, center); charges. For creating the last feature normal distribution with known variance and a logarithmic function, exponential distribution with the identity link function and Pareto distribution with a known scale parameter and a negative linear function were used. Results. The proposed approach was implemented in the form of information processing system for solving the problem of predicting insurance payments based on insurance data and with taking into account the noise of the data. Conclusions. The conducted experiments confirmed that the proposed approach allows for more adequate model constructing and accurate forecasting of insurance payments, which is important point in the analysis of actuarial risks. The prospects for further research may include the use of this approach proposed in other fields of insurance related to availability of actuarial risk. A specialized intellectual decision support system should be designed and implemented to solve the problem by using actual insurance data from real world in online mode as well as modern information technologies and intellectual data analysis. [ABSTRACT FROM AUTHOR]

This article focuses on analyzing the finite-time ruin probability within a specific class of discrete risk models. These models incorporate dependent claims, an interest rate component, and stationary noise terms exhibiting semi-heavy-tailed behavior. In this framework, the claim amount follows a unilateral linear dependent process with independent and identically distributed noise terms, while the discount factor is determined by both the interest rate and time. The finite-time ruin probability has been derived under insurance risk conditions resembling the gamma distribution. [ABSTRACT FROM AUTHOR]

This study introduces the Inverse Burr-X Burr-XII (IBXBXII) distribution as a novel approach for handling asymmetric-bimodal claims and revenues. It explores the distribution's statistical properties and evaluates its performance in three contexts. The analysis includes assessing entropy, highlighting the distribution's significance in various fields, and comparing it to rival distributions using practical examples. The IBXBXII model is then applied to analyze risk indicators in actuarial data, focusing on bimodal insurance claims and income. Simulation analysis shows its preference for right-skewed data, making it suitable for mathematical modeling and actuarial risk assessments. The study emphasizes the IBXBXII model's versatility and effectiveness, suggesting it as a flexible framework for actuarial data analysis, particularly in cases of large samples and right-skewed data. [ABSTRACT FROM AUTHOR]

The ability to run experiments, or to see natural data as a quasi-experiment, does not free one from the need for theory when evaluating insurance behavior. Theory can be used to motivate the experimental design, evaluate latent effects from the experiment, or test hypotheses about latent effects or about observable effects that could be confounded by latent effects. The risk, evident in the broader behavioral literature in general, is the attention given to "behavioral story-telling" in lieu of rigorous scholarship. Such story-telling certainly has a role in fueling speculation about possible casual forces at work generating the data we see, but should not be mistaken for the final word. There is also a severe cost in terms of the heroic assumptions needed for identification. Again, such identifying assumptions can have a valuable role, but many general claims rely critically on those assumptions. Controlled laboratory experiments and Bayesian econometric methods should play a complementary role to field experiments and quasi-experiments. One clear lesson from the evaluation of methodological challenges is to use theory more, to explore the ability of "standard economics" to explain behavior. The time has long passed where straw men theories are set up to fail when confronted with behavior. Just as we want to consider flexible parametric functional forms when appropriate, we should be open to conventional economics applied more flexibly. [ABSTRACT FROM AUTHOR]