Hasil untuk "Insurance"

Fallou Niakh, Alicia Bassière, Michel Denuit et al.

The financial viability of renewable energy projects is challenged by the variability and unpredictability of production due to weather fluctuations. This paper proposes a novel risk management framework combining parametric insurance and peer-to-peer (P2P) risk sharing to address production uncertainty in solar electricity generation. We first design a weather-based parametric insurance scheme to protect against forecast errors, recalibrated at the site level to mitigate geographical basis risk. To handle residual mismatches between insurance payouts and actual losses, we introduce a complementary P2P mechanism that redistributes the remaining basis risk among participants. The method leverages physically based simulation models to reconstruct day-ahead forecasts and realized productions, integrating climate data and solar farm characteristics. A second-order theoretical approximation links heterogeneous local models to a shared weather index, making risk sharing operationally feasible. In an empirical application to 50 German solar farms, our approach reduces the volatility of production losses by 55\%, demonstrating its potential to stabilize revenues and strengthen the resilience of renewable investments.

Hua Zhou, Bing Ma, Yufei Zhang et al.

This paper comprehensively elaborates on the construction methodology, multi-dimensional evaluation system, and underlying design philosophy of CUFEInse v1.0. Adhering to the principles of "quantitative-oriented, expert-driven, and multi-validation," the benchmark establishes an evaluation framework covering 5 core dimensions, 54 sub-indicators, and 14,430 high-quality questions, encompassing insurance theoretical knowledge, industry understanding, safety and compliance, intelligent agent application, and logical rigor. Based on this benchmark, a comprehensive evaluation was conducted on 11 mainstream large language models. The evaluation results reveal that general-purpose models suffer from common bottlenecks such as weak actuarial capabilities and inadequate compliance adaptation. High-quality domain-specific training demonstrates significant advantages in insurance vertical scenarios but exhibits shortcomings in business adaptation and compliance. The evaluation also accurately identifies the common bottlenecks of current large models in professional scenarios such as insurance actuarial, underwriting and claim settlement reasoning, and compliant marketing copywriting. The establishment of CUFEInse not only fills the gap in professional evaluation benchmarks for the insurance field, providing academia and industry with a professional, systematic, and authoritative evaluation tool, but also its construction concept and methodology offer important references for the evaluation paradigm of large models in vertical fields, serving as an authoritative reference for academic model optimization and industrial model selection. Finally, the paper looks forward to the future iteration direction of the evaluation benchmark and the core development direction of "domain adaptation + reasoning enhancement" for insurance large models.

Yong Jun Choi, Aram Yang

BackgroundPrader–Willi syndrome (PWS) is a rare genetic disorder characterized by severe multisystem comorbidities and increased mortality. Although growth hormone therapy (GHT) is widely used as standard care, population-based evidence on its long-term safety, particularly in relation to mortality and type 2 diabetes mellitus (T2DM), remains limited. We aimed to investigate the associations between GHT duration, mortality, and T2DM incidence in PWS.MethodsThis is a nationwide cohort study using the Korean National Health Insurance Service database. A total of 385 individuals with PWS were identified between January 2005 and February 2023. GHT duration was the primary exposure. All-cause mortality was analyzed using Cox proportional hazards models, and T2DM risk was evaluated using multivariable logistic regression adjusted for age, comorbidities, and GHT duration.ResultsGHT duration did not directly impact mortality (OR 1.00, 95% CI: 0.99–1.00); however, peripheral vascular disease (aOR 10.66, 95% CI: 1.07–106.56), renal disease (aOR 17.45, 95% CI: 1.17–259.93), adrenal insufficiency (aOR 23.90, 95% CI: 3.19–178.34), and behavioral disorders (aOR 29.51, 95% CI: 2.64–329.95) were significant predictors of all-cause mortality. Longer GHT duration was independently associated with higher T2DM risk (aOR 1.06, 95% CI: 1.02–1.11). Older age, age at PWS diagnosis, and comorbidities (peptic ulcer disease, mild liver disease, and diabetes insipidus) were additional risk factors.ConclusionsGHT was not a direct predictor of mortality in PWS, which was instead influenced by comorbidities. However, its prolonged use was linked to increased T2DM. These findings support individualized risk assessment and metabolic monitoring in patients with PWS receiving GHT.

Runnan Ha, Linyun Wei, Haoguang Li

This article examines China’s evolving legal framework for ship-induced oil pollution damage liability system, focusing on the 2024 Draft Amendment to the Maritime Law. Historically, the development of this system in China has been achieved through accession to international conventions, domestic legislative transformation, and the improvement of supporting systems. However, fragmented rules and outdated liability limits hindered effective compensation. DAML introduces a dedicated chapter on oil pollution damage, establishing strict liability for shipowners, defining compensation scope, and mandating an “Insurance and Fund” dual safeguard system. It further clarifies joint liability for multi-ship spills and conflict-of-law rules favoring the lex loci damni. The study argues that this revision bridges gaps between international standards and domestic law while addressing judicial inconsistencies. In order to refine this system, this article conducts an analysis in conjunction with DAML revision text and puts forward optimization suggestions from four perspectives: improving the legal system, providing case law guidance, enhancing government supervision, and perfecting supporting legal safeguards.

Kedir Hussein Abegaz, Trhas Tadesse Berhe, Alachew Wondie Tamrat et al.

Background In developing countries such as Ethiopia, financial barriers to accessing medical treatments have placed at significant risk of social and economic hardship. To address these critical issues, this study aimed to determine the satisfaction level of community-based health insurance (CBHI) members in Akaki Kality Sub City, Addis Ababa, Ethiopia, in 2023.Method A community-based cross-sectional study was conducted from 22 May 2023 to 22 June 2023, with 630 participants. A multistage sampling method was used by selecting 50% of districts and 30% of ‘subdistricts’. Systematic random sampling (with K=5) was employed for participant selection. Data were entered into Epi-data V.3.1 and analysed using SPSS V.25. Descriptive statistics were calculated to determine the level of satisfaction. Bivariate logistic regression was applied to assess the association with p values <0.25. Those variables which showed significant association were included in multivariate analysis. To control confounders, multivariate analysis was conducted with a p<0.05.Result The study revealed that the overall satisfaction for the -CBHI scheme was 53.5% (95% CI 49%, 57%). Factors influencing satisfaction included average monthly income (adjusted OR (AOR) 0.62; 95% CI 0.43, 0.88), frequency of health facility visits (AOR 0.34; 95% CI 0.25, 0.90), availability of prescribed drugs (AOR 1.77, 95% CI 1.08, 2.93) and perceiving CBHI as having valid management regulations (AOR 1.74; 95% CI 1.12, 3.46).Conclusions The study demonstrated that the overall satisfaction level with the CBHI scheme was relatively low compared with other studies. Measures should include enhancing medication and laboratory test accessibility and reinforcing CBHI management and regulatory processes through increased manpower and improved monthly income level.

Smagulov Alibek, Kurakbayev Kuralbay, Baimakhanov Abylai et al.

BackgroundIn Kazakhstan, transformation of the private healthcare sector as the country transitioned from lower-middle-income to upper-middle-income status has rarely been a subject of academic debate. This study aimed to analyze of health sector indicators disaggregated by type of ownership over the period of 10 years (from 2011 to 2020).MethodsThis was a retrospective cross-sectional study, which was based on official healthcare statistics presented by the Ministry of Health. Relative change (RC) was computed to identify trends in changes over time and was expressed as a proportion with 95% confidence intervals.ResultsIn contrast with the government-owned health facilities, the overall number of private facilities increased over time, although this growth was less obvious in per capita terms. The number and density of private PHC facilities grew more substantially than those of outpatient facilities. PHC practitioners employed by the government-owned facilities admit more patients than practitioners of the private PHC facilities do. There is an uneven presence of the private health sector in different medical specialties with maternity, ophthalmology, dentistry, narcology, multidisciplinary, as well as palliative and nursing care being the most common.ConclusionSuch data are needed by decision-makers to tailor public health strategies focused on the stewardship of the private health sector, which would help improve the availability and affordability of medical services.

Pascal Petit, François Berger, Vincent Bonneterre et al.

Abstract The risk of Parkinson’s disease (PD) associated with farming has received considerable attention, in particular for pesticide exposure. However, data on PD risk associated with specific farming activities is lacking. We aimed to explore whether specific farming activities exhibited a higher risk of PD than others among the entire French farm manager (FM) population. A secondary analysis of real-world administrative insurance claim data and electronic health/medical records (TRACTOR project) was conducted to estimate PD risk for 26 farming activities using data mining. PD cases were identified through chronic disease declarations and antiparkinsonian drug claims. There were 8845 PD cases among 1,088,561 FMs. The highest-risk group included FMs engaged in pig farming, cattle farming, truck farming, fruit arboriculture, and crop farming, with mean hazard ratios (HRs) ranging from 1.22 to 1.67. The lowest-risk group included all activities involving horses and small animals, as well as gardening, landscaping and reforestation companies (mean HRs: 0.48–0.81). Our findings represent a preliminary work that suggests the potential involvement of occupational risk factors related to farming in PD onset and development. Future research focusing on farmers engaged in high-risk farming activities will allow to uncover potential occupational factors by better characterizing the farming exposome, which could improve PD surveillance among farmers.

Zihao Qiu, Siyu Chen, Zixin Feng et al.

Futures, as significant financial derivatives, play a crucial role in financial markets by fulfilling price discovery functions and providing efficient risk hedging tools. Against the backdrop of geopolitical conflicts, market risk emerges not only from external shocks and random fluctuations but also from strategic interactions among diverse participants including hedgers, speculators, arbitrageurs, and regulators. This study integrates traditional VaR theory with machine learning methods to systematically examine risk characteristics and transmission mechanisms in the sugar futures market under geopolitical uncertainty. Utilizing sugar No. 5 futures trading data from the Zhengzhou Futures Exchange spanning 2015–2019 and 2024, we employ a Random Forest model for feature importance analysis and compare three risk measurement approaches: traditional parametric VaR, historical simulation methods, and machine learning-enhanced VaR models. We conduct empirical tests to validate the theoretical relationship √3 × VaRT(1,p) ≈ VaRT(3,p) and calculate epsilon values (relative deviation between actual and estimated tail risk occurrences) through return tests. Annual delta values range between 0.26 and 1.16, averaging approximately 35% below theoretical values. The machine learning-based Value at Risk (VaR) at 95% confidence level exhibits a violation rate of 5.00%, demonstrating superior accuracy compared to parametric VaR (26.67%) and traditional historical VaR (7.00%). Epsilon values show no statistically significant difference between 2024 (0.08) and the 2015–2019 average level (0.14), indicating stable risk transmission mechanisms despite geopolitical conflicts. The hybrid “machine learning-traditional theory” risk framework developed in this research provides a theoretical foundation and practical guidance for regulatory bodies to enhance risk prevention and control systems, as well as for market participants to optimize risk management strategies. Despite geopolitical impacts, the fundamental risk transmission mechanisms of the sugar futures market remain relatively stable, demonstrating market resilience.

Tetyana Ivanova, Halyna Kryshtal, Volodymyr Metelytsia et al.

This article analyzes the implementation of investment-oriented innovations in the field of medical insurance to establish an effective and sustainable healthcare financing model amidst digital economic transformation. The authors provide theoretical justification for the role of innovations and investments in the development of the medical insurance sector, along with a deep analysis of the current state of medical services and the main barriers hindering innovation in this area. Special attention is given to assessing the impact of digitalization on the accessibility and quality of medical services, as well as the potential of using advanced technologies such as telemedicine and online platforms for insurance companies. It is considered that digital technologies, including telemedicine and online services, have the potential to significantly improve access to medical services and reduce costs. However, the implementation of these innovations faces several challenges, including issues of internet access, the lack of proper legal frameworks for regulating digital technologies in medical insurance, and the need to enhance financial literacy among the population. The authors summarize and propose a list of barriers (technical, regulatory, financial, cultural, psychological barriers, barriers related to human resources, and competition-related barriers) in attracting investments to the sector. Practical recommendations are offered for stakeholders, including insurance companies, government bodies, and users of medical services, to facilitate more effective innovation implementation and ensure sustainable financing of medical services. The strategic approaches to sustainable healthcare financing proposed in the article aim to ensure equal access to medical services for all population segments, improve the quality of insurance services, and optimize costs through digital tools.

Xingyuan Liang, Haibao Wen

This study explores the conceptual development of a medical insurance catalogue voting system. The methodology is centred on creating a model where doctors would vote on treatment inclusions, aiming to demonstrate transparency and integrity. The results from Monte Carlo simulations suggest a robust consensus on the selection of medicines and treatments. Further theoretical investigations propose incorporating a patient outcome-based incentive mechanism. This conceptual approach could enhance decision-making in healthcare by aligning stakeholder interests with patient outcomes, aiming for an optimised, equitable insurance catalogue with potential blockchain-based smart-contracts to ensure transparency and integrity.

Edward James Young, Alistair Rogers, Elliott Tong et al.

When faced with a new customer, many factors contribute to an insurance firm's decision of what offer to make to that customer. In addition to the expected cost of providing the insurance, the firm must consider the other offers likely to be made to the customer, and how sensitive the customer is to differences in price. Moreover, firms often target a specific portfolio of customers that could depend on, e.g., age, location, and occupation. Given such a target portfolio, firms may choose to modulate an individual customer's offer based on whether the firm desires the customer within their portfolio. We term the problem of modulating offers to achieve a desired target portfolio the portfolio pursuit problem. Having formulated the portfolio pursuit problem as a sequential decision making problem, we devise a novel reinforcement learning algorithm for its solution. We test our method on a complex synthetic market environment, and demonstrate that it outperforms a baseline method which mimics current industry approaches to portfolio pursuit.

Jingyi Cao, Dongchen Li, Virginia R. Young et al.

We find the optimal indemnity to maximize the expected utility of terminal wealth of a buyer of insurance whose preferences are modeled by an exponential utility. The insurance premium is computed by a convex functional. We obtain a necessary condition for the optimal indemnity; then, because the candidate optimal indemnity is given implicitly, we use that necessary condition to develop a numerical algorithm to compute it. We prove that the numerical algorithm converges to a unique indemnity that, indeed, equals the optimal policy. We also illustrate our results with numerical examples.

Na Ren, Xin Zhang

The frequent occurrence of cyber risks and their serious economic consequences have created a growth market for cyber insurance. The calculation of aggregate losses, an essential step in insurance pricing, has attracted considerable attention in recent years. This research develops a path-based k-generation risk contagion model in a tree-shaped network structure that incorporates the impact of the origin contagion location and the heterogeneity of security levels on contagion probability and local loss, distinguishing it from most existing models. Furthermore, we discuss the properties of k-generation risk contagion among multi-paths using the concept of d-separation in Bayesian network (BN), and derive explicit expressions for the mean and variance of local loss on a single path. By combining these results, we compute the mean and variance values for aggregate loss across the entire network until time $t$, which is crucial for accurate cyber insurance pricing. Finally, through numerical calculations and relevant probability properties, we have obtained several findings that are valuable to risk managers and insurers.

Halyna Kryshtal , Viktoriia Tomakh , Tetiana Ivanova et al.

The study is aimed at summarizing the processes of eco-innovative (green) transformation of urban infrastructure and researching possible prospects for the development of Ukraine in this context. In the course of the research, the possibilities of "green" transformation of urban infrastructure were considered and it was noted that the use of the principles of eco-innovative transformation in the post-war period can only take place under the condition of proper planning, state support and the creation of favourable market conditions. The authors noted that the success of such a transformation requires the establishment of green goals in all aspects of the development of Ukrainian cities. Auto-frame considered the financial possibilities of the development of urban infrastructure and proposed the location of support offices for the eco-innovative transformation of urban infrastructure at the regional level. The principles of achieving eco-innovative transformation of urban infrastructure are revealed, namely maximum energy efficiency, energy transition, "zero waste", environmental sustainability of buildings, adaptation to climate change, popularization of a green lifestyle, resource conservation, citizen involvement and circular economy. It is proposed to create a platform that would unite architects, builders, urban planners, citizens, artists and other interested persons. This platform should contribute to the search for answers to the question of how to ensure a quick, ecological, attractive and safe "green" transformation of urban infrastructure. Ukraine should cooperate with the European Union within various green platforms and networks that help cities in green transformation. All the above-mentioned tools and solutions should contribute to the creation of green, sustainable and people-oriented cities in Ukraine. The authors have considered the possibilities of financing the restoration of Ukrainian cities after the destruction in terms of the necessary financial resources, donor countries, and reconstruction expenditures.

Xu Han, Maria Koliou

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Volodymir Kotsyuruba, Іhor Proshchyn

Мета роботи: удосконалити методику прогнозування наслідків надзвичайних ситуацій на гідротехнічних спорудах терористичного характеру. Метод дослідження: основними методами досліджень є методи : аналізу та синтезу. Теоретична цінність дослідження: Запропонована методика має суттєве значення для теорії цивільного захисту та може бути використана не лише для проведення розрахунків при прогнозуванні масштабів та обсягів негативного впливу наслідків зруйнування гідротехнічних споруд, а і  для проведення наступних наукових досліджень.. Практична цінність дослідження: дана методика дає можливість враховувати міграційні процеси населення та темпи розбудови урбанізованої місцевості при прогнозуванні параметрів надзвичайних ситуацій терористичного характеру на гідротехнічних спорудах. Цінність дослідження: розроблена методика враховує зниження прохідності місцевості, неоднорідність щільності забудови урбанізованої місцевості та густини заселеності районів виникнення надзвичайних ситуацій в межах зон затоплень Тип статті: практичний.

Faraz Nabiyi , Mehdi Shamizanjani, Nima Garoosi Mokhtarzadeh

Today's world has been affected by digital technologies more than ever, and no business can be considered independent of these technologies, regardless of industry, size, and geography. Various industries such as banking, insurance, automobile, petrochemical, steel, energy, food, entertainment and education are experiencing a fundamental change centered on digital technologies. In the current era, organizations have entered a new era in which digital technologies have revolutionized the majority of industries, from products and services to customer expectations. Today's organizations have practically no other way to survive and that is digital transformation. Digital transformation is an inevitable necessity and a vital strategic issue for today's businesses, which will suffer great losses if they do not adopt and implement the right strategy and consequently they might fail.By reviewing the theoretical academic resources, despite the need for a model to formulate a digital transformation strategy which there is a consensus on and contains various dimensions of digital transformation with an exhaustive view, there has never been such a framework, which reminds us of the need to address this issue. This research is designed to address the Conceptualization of digital transformation strategy as mentioned earlier. In this research, by conceptualizing the digital transformation strategy and cognition of its various dimensions, the mentioned gap will be addressed, by using the research method of meta synthesis. For this purpose, after reviewing 17 selected articles, a specific definition of digital transformation strategy has been presented and its relationship with business strategy and functional strategies has been analyzed. Also, the principles of shaping the digital transformation strategy are presented in two content and process categories. Finally, three categories of cultural, structural and leadership capabilities have been identified as supporting capabilities for shaping the digital transformation strategy.

Baker Alhasn, Mohammad Qatawneh, Wesam Alkmobaideen

The paper proposes a Blockchain (BC) system to prevent counterfeiting in health insurance sector. The results show the system strength in terms of achieving data integrity and privacy of data. Moreover, the results show that the consensus algorithm can effectively reduce the total validation time for the proposed system.

Mostafa Shams Esfand Abadi, Kaushik Ghosh

The prediction of future insurance claims based on observed risk factors, or covariates, help the actuary set insurance premiums. Typically, actuaries use parametric regression models to predict claims based on the covariate information. Such models assume the same functional form tying the response to the covariates for each data point. These models are not flexible enough and can fail to accurately capture at the individual level, the relationship between the covariates and the claims frequency and severity, which are often multimodal, highly skewed, and heavy-tailed. In this article, we explore the use of Bayesian nonparametric (BNP) regression models to predict claims frequency and severity based on covariates. In particular, we model claims frequency as a mixture of Poisson regression, and the logarithm of claims severity as a mixture of normal regression. We use the Dirichlet process (DP) and Pitman-Yor process (PY) as a prior for the mixing distribution over the regression parameters. Unlike parametric regression, such models allow each data point to have its individual parameters, making them highly flexible, resulting in improved prediction accuracy. We describe model fitting using MCMC and illustrate their applicability using French motor insurance claims data.

Olivier Lopez, Maud Thomas

Parametric insurance has emerged as a practical way to cover risks that may be difficult to assess. By introducing a parameter that triggers compensation and allows the insurer to determine a payment without estimating the actual loss, these products simplify the compensation process, and provide easily traceable indicators to perform risk management. On the other hand, this parameter may sometimes deviate from its intended purpose, and may not always accurately represent the basic risk. In this paper, we provide theoretical results that investigate the behavior of parametric insurance products when faced with large claims. In particular, these results measure the difference between the actual loss and the parameter in a generic situation, with a particular focus on heavy-tailed losses. These results may help to anticipate, in presence of heavy-tail phenomena, how parametric products should be supplemented by additional compensation mechanisms in case of large claims. Simulation studies, that complement the analysis, show the importance of nonlinear dependence measures in providing a good protection over the whole distribution.