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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]

The Geneva Risk and Insurance Review celebrates in 2016 its 40th year of existence. Yes. But it must be recalled that about half of these 40 years occurred under a different name-The Geneva Papers on Risk and Insurance. For a long time, the fate of the journal was very closely linked to the development of 'The Geneva Association'. [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]

In particular, this paper examines whether insurers with greater opportunities to manage care report significantly better underwriting performance in the fourth quarter relative to other health insurers. Health risk can be defined as the likelihood of a negative health consequence occurring due to a specific event, disease or condition. The first two papers address supply-side issues of health insurance markets by investigating the behaviour and performance of health insurers, either in relation to HMOs or in serving poor individuals. [Extracted from the article]

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]

This is the third special issue of I The Geneva Papers on Risk and Insurance - Issues and Practice i devoted to cyber risk and cyber insurance (previous issues were published in April 2018 and October 2020). They describe the origins of cyber insurance and different generations of ransomware-as-a-service, emphasising the industrialisation of cybercrime that insurers are facing. They also illustrate the evolutionary dynamic of insurance markets and how insurers shape and respond to changes in the risk environment in which they operate. The conceptual foundation of the paper is the "insurance-as-governance" literature, which demonstrates that insurers often make insurance conditional on ex ante risk reduction or mitigation. [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]

CAT bonds may add value to insurers' risk management strategies and may therefore substitute for reinsurance. Physical climate risks associated with extreme weather events, such as tropical cyclones, heatwaves and floods, and slow-changing conditions, such as sea level rise and prolonged higher temperatures, are increasing due to climate change. This thematic issue of I The Geneva Papers i contributes to this understanding by addressing four important issues: the awareness of insurers of climate risks and opportunities, the role of insurance in improving disaster risk preparedness, optimal public-private insurance schemes to cover natural catastrophes, and finally the use of reinsurance and CAT bonds as natural catastrophe risk transfer tools. [Extracted from the article]

Vietnam is exposed to different types of floods that cause severe economic losses, damage to infrastructure, and loss of life. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Vietnam. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in the country. The literature review revealed that: (i) 65 % of the reviewed papers did not provide a clear definition of flood risk, (ii) assessments had a tendency to prioritize physical and environmental drivers of risk over social, economic or governance‐related drivers, (iii) future‐oriented assessments tended to focus on hazard and exposure trends, while vulnerability scenarios were often lacking, (iv) large and middle‐sized cities were assessed more frequently than others, (v) only few studies engaged with relevant local stakeholders for the assessment of risk and the development of potential solutions, and (vi) ecosystem‐based adaptation and flood risk insurance solutions were rarely considered. Based on these findings, we point out several directions for future research on flood risk in Vietnam. [ABSTRACT FROM AUTHOR]

The agricultural sector in Moldova is highly exposed to natural factors and hazards, resulting in uncertain incomes and high production volatility. Crop insurance plans against different risks plays a crucial role in supporting farmers' yield and revenues. The aim of this paper is to assess the development of subsidizing production risk insurance policy as a method to enhance agricultural resilience and alleviate the effects of climate change in Moldova. The analyzed data is based on the analysis of regulatory documents for policies regarding subsidizing productions risks insurance. For data analysis, secondary data provided by the National Bureau of Statistics (NBS), and Agency of Interventions and Payments in Agriculture (AIPA) was used. The data refer to the amount of subsidies applications for risks insurance, insured areas cultivated with different crops and its harvests, main risks insured by farmers, distribution of subsidies for risks insurance by development regions and the insurance companies. The subsidization of crop production risk insurance in Moldova has played a vital role in bolstering agricultural resilience and minimizing the adverse effects of climate change. By offering assistance to farmers and promoting the implementation of risk management practices, the government's objective is to stabilize income levels and promote sustainable growth in the agricultural sector. Nevertheless, it is crucial to continuously monitor and evaluate these policies to ensure their efficacy and fair distribution of subsidies across various regions and insurance providers. [ABSTRACT FROM AUTHOR]

To improve the effect of commercial insurance risk decision, this paper applies neural network algorithms to commercial insurance risk decision under the guidance of machine learning ideas, and selects the neural network algorithm based on the actual situation. Moreover, this paper analyzes the nature of risks of commercial insurance, analyzes the types of risks and risk relevance, constructs a commercial insurance risk decision model based on neural network algorithms, and determines the system process. In addition, this paper uses a combination method of qualitative and quantitative to identify the influencing factors of risk estimation to obtain relevant influencing factors, and verify the model proposed in this paper in combination with experimental research. From the experimental research results, it can be seen that the commercial insurance risk decision system based on neural network algorithm is very good in terms of decision effect. [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 paper contributes to the literature by showing the progress made in deposit insurance and risk domain in the banking sector using a bibliometric approach. A conjoint study of deposit insurance and risk yields a broader view on the research trend progression. The analysis shows that niche themes around banking regulation, Prompt Corrective Action (PCA), and emerging markets have evolved. Banking regulations form a cornerstone of financial stability, with PCA progressively showing predominance, reflecting early warning signals of a possible banking failure while deposit insurance may be a key tool to prevent the impact of such failure from reaching the consumers, it does not improve the protection available to other stakeholders which presents a corporate governance challenge of balancing conflicting interests of different stakeholders in the banking system. [ABSTRACT FROM AUTHOR]

In the fourth essay, I Hong Fu, Yuehua Zhang, Yinuo An, Li Zhou, Yanling Peng, Rong Kong i , and I Calum G. Turvey i consider how belief formation about risk can influence insurance markets. The original online version of this article was revised: Not all authors were listed correctly as authors of this paper and the footnote on the first page was missing. [Extracted from the article]

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]

The inverse moment's research can be used in many fields such as risk evaluation and insurance mathematics. In this paper, we consider the weighted inverse moment model based on asymptotically almost negatively associated (AANA, in short) sequences. Under some weak condition, we obtain the asymptotic approximation of inverse moments and its convergence rate. As applications of inverse moments, we study the random ratio models which contain the change-point detection models, and obtain the asymptotic moment approximation of random ratio and its convergence rate. Our conditions of inverse moment and random ratio models are very weaker than the existing models. In order to test our results, we do some simulations and real data analysis in this paper. Our results improve and general the existing works. [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]

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]

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]

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]

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 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]

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]

This article provides an overview of all papers published on the special issue, Advances in Actuarial Science and Quantitative Finance. The special issue is intended to collect articles that reflect the latest development and emerging topics in these closely related two areas. Topics included in this special issue range from actuarial and risk theory, to optimal control for finance and insurance, to statistical inferences of financial and insurance models, to pricing, valuation and reserving. [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]

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This study aims to examine the mediating effect of firm risk on the relationships between board structure and firm performance. The multivariate panel data regression technique is employed to analyse the mediating impact of firm risk on 27 listed insurance companies on the Saudi Stock Exchange (Tadawul) from 2016 to 2021. The findings of this study indicate that firm risk partially mediates the relationship between audit independence and Tobin's Q. In contrast to the existing literature, the study reveals that boards composed of independent members may lack effectiveness in their monitoring role, leading to higher risk-taking behaviour. This paper contributes to the literature on corporate governance and firm performance by examining the association through the lens of firm risk. [ABSTRACT FROM AUTHOR]

Emerging environmental sustainability issues require governments to develop effective policies to respond to them appropriately. The insurance industry can contribute to the risk management of climate change and environmental issues by offering green insurance products. In this study, a bibliometric analysis was conducted to research the articles investigating the policy implications of different types of green, environmental, and sustainability insurance as risk management, risk mitigation, and damage or impact assessment techniques applied in diverse practices. Bibliometric analysis approaches, such as bibliographic coupling and co-occurrence analyses, were utilized. The analysis revealed seven key research themes. Recommendations for future research include investigating the impacts and development of assessment instruments of environmental, sustainable, and green insurance and their various products. [ABSTRACT FROM AUTHOR]

In this study, we assess COVID-19-related mortality in Minnesota and Wisconsin with the aim of demonstrating both the temporal dynamics and the magnitude of the pandemic's influence from an actuarial risk standpoint. In the initial segment of this paper, we discuss the methodology successfully applied to describe associations in financial and engineering time series. By applying time series analysis, specifically the autoregressive integrated with moving average methods (ARIMA), to weekly mortality figures at the national or state level, we subsequently delve into a marginal distribution examination of ARIMA residuals, addressing any deviation from the standard normality assumption. Thereafter, copulas are utilized to architect joint distribution models across varied geographical domains. The objective of this research is to offer a robust statistical model that utilizes observed mortality datasets from neighboring states and nations to facilitate precise short-term mortality projections. In the subsequent section, our focus shifts to a detailed scrutiny of the statistical interdependencies manifesting between Minnesota and Wisconsin's weekly COVID-19 mortality figures, adjusted for the time series structure. Leveraging open-source data made available by the CDC and pertinent U.S. state government entities, we apply the ARIMA methodology with subsequent residual distribution modeling. To establish dependence patterns between the states, pair copulas are employed to articulate the relationships between the ARIMA residuals, drawing from fully parametric models. We explore several classes of copulas, comprising both elliptic and Archimedean families. Emphasis is placed on copula model selection. Student t-copula with the marginals modeled by non-standard t-distribution is suggested for ARIMA residuals of Minnesota and Wisconsin COVID mortality as the model of choice based on information criteria and tail cumulation. The copula approach is suggested for the construction of short-term prediction intervals for COVID-19 mortality based on publicly available data. [ABSTRACT FROM AUTHOR]

In this paper, we explore potential surplus modelling improvements by investigating how well the available models describe an insurance risk process. To this end, we obtain and analyse a real-life data set that is provided by an anonymous insurer. Based on our analysis, we discover that both the purchasing process and the corresponding claim process have seasonal fluctuations. Some special events, such as public holidays, also have impact on these processes. In the existing literature, the seasonality is often stressed in the claim process, while the cash inflow usually assumes simple forms. We further suggest a possible way of modelling the dependence between these two processes. A preliminary analysis of the impact of these patterns on the surplus process is also conducted. As a result, we propose a surplus process model which utilises a non-homogeneous Poisson process for premium counts and a Cox process for claim counts that reflect the specific features of the data. [ABSTRACT FROM AUTHOR]

We analyze overall mortality in the U.S. as a whole and several states in particular in order to make conclusions regarding timing and strength of COVID pandemic effect from an actuarial risk analysis perspective. No effort is made to analyze biological or medical characteristics of the pandemic. We use open data provided by CDC, U.S. state governments and Johns Hopkins University. In the first part of the paper, we suggest time series analysis (ARIMA) for weekly excess U.S. mortality in 2020 as compared to several previous years' experience in order to build a statistical model and provide short-term forecast based exclusively on historical mortality data. In the second half of the paper, we also analyze weekly COVID cases, hospitalizations and deaths in 2020 and 2021. Two midwestern states, Minnesota and Wisconsin, along with geographically diverse Colorado and Georgia, are used to illustrate global and local patterns in the COVID pandemic data. We suggest vector autoregression (VAR) as a method of simultaneous explanatory and predictive analysis of several variables. VAR is a popular tool in econometrics and financial analysis, but it is less common in problems of risk management related to mortality analysis in epidemiology and actuarial practice. Efficiency of short-term forecast is illustrated by observing the effect of vaccination on COVID development in the state of Minnesota in 2021. [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]

A relevant definition of big data technology has been released by relevant research institutes in China. Big data technology is defined as follows: big data technology is a digitally important productivity factor in the network era; big data technology is based on network technology, statistical technology, and mathematical technology; big data technology, as a carrier of network technology, should be integrated with various industries and technologies other than contemporary ones in order to achieve better informatization and industrialization. In the context of the new crown epidemic in 2020, China's big data technology continues to grow steadily, with a total value of 39.2 trillion yuan, accounting for 38.6% of GDP. This paper investigates the risk constraint in the context of big data and optimizes the benign interaction mechanism of insurance management based on distorted risk metrics in this context. The current situation of insurance risk is investigated through a profound discussion of theories in this paper and through research methods such as distortion risk metrics and SQL Server database management. It is found that the overall situation of insurance industry development in the era of digital economy is not optimistic and so on. And relevant suggestions are made in the conclusion. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present the case of an Italian SME in the domain of insurance and how it approached its own digital transformation. Together with the founders of the SME, the author investigated the digital trends the company should adopt and identified where to intervene in the value chain of the company with new technologies available in the market. The research was focused on the following three sub-domains: a strategy for adoption of innovative digital solutions to improve the everyday operations of the company, platform connecting the company with the customers and analysis of cyber insurance policies to include in the portfolio of the company. Design/methodology/approach: For the part on strategy for adoption of innovative digital solutions, the author performed literature review; for the part in which the study ideates new solution to better connect the company with the customers, the author relied on design thinking, creative facilitation and prototyping; and for the part on cyber insurance policies to include the portfolio, the author relied on data available from other insurance companies the SME collaborates with. Findings: This paper presented the analysis on how an insurance SME can embrace digital innovation (via internal innovation, buying from startups, partnering with startups or investing in startups), how an SME can do internal innovation and come up with a simple tool to bring closer the insurers and their customers and types of new cyber risk policies to include in the portfolio to respond to the growing demand for cyber risk insurance. This paper provides useful insights and lessons learned from companies of similar size in the domain of insurance and discusses future extensions of inquiry. Originality/value: Big insurance companies and incumbent for their digitization efforts rely on the freshly created InsurTechs wave of companies. In this paper, the author analyzes what small- and medium-sized insurance enterprises can do in this respect and showcases the approach an Italian SME took in this direction. [ABSTRACT FROM AUTHOR]

In this paper we propose an new approach to multivariate collective models based on asymptotic distibutions, since the modeling problems posed have large samples. Collective risk models play an important part in Risk Theory and in Actuarial Mathematics. Inference based on these models is centered on claims totals. However, the new approach has in mind a different kind of risk, the risk of forest fires, where the variables of interest are the number fires and the total burnt areas. As a result, a special case bivariate risk model is derived with this intent. Besides single models, structured families whose models correspond to the treatments of a base linear model are considered. This leads to an ANOVA-like situation where we don't have to estimate the error, which enable to test the influence of several factors on the multivariate collective models mean vectors. Instead of F tests, we use χ 2 tests, availing us of asymptotic distributions that lightens the treatment. To illustrate the approach, an application to forest fires in Portugal with real data is presented. [ABSTRACT FROM AUTHOR]

This paper combines reference point techniques and econometric analyses to provide the profile of non‐life insurers that simultaneously optimize the strategic growth, profitability, and risk goals. The econometric analyses provide the relevant relations among the variables. Non‐life insurers from 33 Organisation for Economic Co‐operation and Development countries over a six‐year period are analyzed. A cluster analysis allows forming groups of countries according to the non‐life insurance penetration ratio. Several scenarios, which are characterized by the maturity of the market and the crisis/non‐crisis situation, are studied. The results indicate that the highest level of profitability (growth) is linked to scenarios with a medium (low) level of maturity and booming times. They also show that the lowest level of risk that is representative of good performance is associated with scenarios where markets have a high level of maturity and crisis times. We find that a higher recommendable size is associated with more mature markets. The results also indicate that reinsurance utilization is linked to a crisis time. We additionally find that the recommendable level of capitalization differs significantly among scenarios. [ABSTRACT FROM AUTHOR]

The paper investigates a family of robust reinsurance-investment strategies under relative performance criteria for the insurers who have exponential utility and trade in a common reinsurance-investment horizon in log-normal markets. Assume that two correlated Brownian motions used to characterize the insurance surplus risk and stock price risk. By applying stochastic control theories, we derive the closed-form solutions for the n-players game and the corresponding mean field game, furthermore the equilibria strategies are shown to be unique in the class of constant equilibria. Finally, we present the influence of model parameters on reinsurance and investment strategies with economic explanations by some numerical figures. [ABSTRACT FROM AUTHOR]

This paper exploits the representation of the conditional mean risk sharing allocations in terms of size-biased transforms to derive effective approximations within insurance pools of limited size. Precisely, the probability density functions involved in this representation are expanded with respect to the Gamma density and its associated Laguerre orthonormal polynomials, or with respect to the Normal density and its associated Hermite polynomials when the size of the pool gets larger. Depending on the thickness of the tails of the loss distributions, the latter may be replaced with their Esscher transform (or exponential tilting) of negative order. The numerical method then consists in truncating the series expansions to a limited number of terms. This results in an approximation in terms of the first moments of the individual loss distributions. Compound Panjer-Katz sums are considered as an application. The proposed method is compared with the well-established Panjer recursive algorithm. It appears to provide the analyst with reliable approximations that can be used to tune system parameters, before performing exact calculations. [ABSTRACT FROM AUTHOR]

Suppose that risk reserves of an insurance company are governed by a Markov-modulated classical risk model with parameters modulated by a finite-state irreducible Markov chain. The main purpose of this paper is to calculate ultimate ruin probability that ruin time, the first time when risk reserve is negative, is finite. We apply Banach contraction principle, q-scale functions and Markov property to prove that ultimate ruin probability is the fixed point of a contraction mapping in terms of q-scale functions and that ultimate ruin probability can be calculated by constructing an iterative algorithm to approximate the fixed point. Unlike Gajek and Rudź (Insurance: Mathematics and Economics, 80 (2018), 45–53), our paper uses q-scale functions to obtain more explicit Lipschitz constant in Banach contraction principle in our case so that proofs of several Lemmas and theorems in their Appendix are unnecessary and some of their assumptions are confirmed in our case. [ABSTRACT FROM AUTHOR]

401(k) plans provide little guidance on turning accumulated assets into income. Insurance against the risk of outliving one's assets is available through immediate annuities, deferred annuities, and additional Social Security through delayed claiming. Under this Social Security bridge option, participants would tap their 401(k) for payments equal to their Social Security to delay claiming. This paper compares these three options in simulations against a baseline in which no assets are annuitized. In each option, assets not allocated to purchasing lifetime income are consumed following the required minimum distribution; robustness of results to optimal drawdown conditional on annuitization strategy is also assessed. The analysis finds that, when market and health shocks are included alongside longevity uncertainty, the Social Security bridge option is generally the best for households with median wealth. Wealthier households can benefit from combining the bridge option with a deferred annuity. [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]

The purpose of this paper is to explore the short-term economic impacts of the COVID-19 pandemic on the insurance market with particular emphasis on the Western Balkan countries. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus (SARS-CoV-2 coronavirus). This is a rare event, one we have not seen in 100 years, with major consequences for public health, economics and politics. The decline in purchasing power, the economic downturn, the decline in consumption, the rise in inflation will have a major impact on the development of the insurance market. Demand for insurance products worldwide has declined: whether health, life or non-life. The paper will trace the ability of insurance companies to manage the risks posed by the Covid-19 pandemic. Consumer protection, protection of insurance contracts, investment risk management, solvency risk management and inflationary risk. The complete sample of the work will have the insurance market in the Western Balkans. The paper will be drafted based on statistical, analytical and comparative methods. The insurance market in these countries is small, but enables a study on the impact of the Covid-19 pandemic on insurance activity. [ABSTRACT FROM AUTHOR]

A single-parameter Pareto model, Pareto I, arises in many areas of application such as pricing of insurance risks, measuring income or wealth inequality in economics, or modelling lengths of telephone calls in telecommunications. In insurance, for example, it is common to work with data that are truncated (due to deductibles), censored (due to policy limits), and contaminated by outliers (due to model misspecification). Therefore, it is prudent to estimate Pareto I using robust procedures that are designed for such data transformations and are resistant to outliers. In this paper, we consider trimmed (T) and winsorized (W) estimators for the Pareto tail index α, and conduct a simulation study to check these estimators' performance in finite samples. Two broad aspects are investigated: Convergence of α ˆ 's to α as sample size increases from n = 25 to n = 1000. This is evaluated by presenting a series of boxplots and measuring each estimator's relative bias and finite-sample relative efficiency (with respect to the asymptotic variance of the maximum likelihood estimator, MLE). Sensitivity of α ˆ 's and of two value-at-risk estimates to a new observation (an outlier) that is placed at various locations within the sample ranging from the point of left-truncation/-censoring to the point of right-censoring. We find that with respect to (a), T- and W-estimators perform best when n is small and MLE outperforms its competitors when n is large. And with respect to (b), MLE is not robust whilst T's and W's can offer any desired degree of robustness. [ABSTRACT FROM AUTHOR]