Hasil untuk "Insurance"

N. Duan

Michael Schmutz, Eckhard Platen, Thorsten Schmidt

In this paper we study the pricing and hedging of nonreplicable contingent claims, such as long-term insurance contracts like variable annuities. Our approach is based on the benchmark-neutral pricing framework of Platen (2024), which differs from the classical benchmark approach by using the stock growth optimal portfolio as the numéraire. In typical settings, this choice leads to an equivalent martingale measure, the benchmark-neutral measure. The resulting prices can be significantly lower than the respective risk-neutral ones, making this approach attractive for long-term risk-management. We derive the associated risk-minimizing hedging strategy under the assumption that the contingent claim possesses a martingale decomposition. For a set of nonreplicable contingent claims, these strategies allow monitoring the working capital required to generate their payoffs and enable an assessment of the resulting diversification effects. Furthermore, an algorithmic refinancing strategy is proposed that allows modeling the working capital. Finally, insurance-finance arbitrages of the first kind are introduced and it is demonstrated that benchmark-neutral pricing effectively avoids such arbitrages.

Coleman Drake, Mark K. Meiselbach, Daniel Polsky

Enrollment in the Health Insurance Marketplaces created by the Affordable Care Act reached an all-time high of approximately 25 million Americans in 2025, roughly doubling since enhanced premium tax credit subsidies were made available in 2021. The scheduled expiration of enhanced subsidies in 2026 is estimated to leave over seven million Americans without health insurance coverage. Ten states have created supplemental Marketplace subsidies, yet little attention has been paid to how to best structure these subsidies to maximize coverage. Using administrative enrollment data from Maryland's Marketplace, we estimate demand for Marketplace coverage. Then, using estimated parameters and varying budget constraints, we simulate how to optimally allocate supplemental state premium subsidies to mitigate coverage losses from enhanced premium subsidy expiration. We find that premium sensitivity is greatest among enrollees with incomes below 200 percent of the federal poverty level, where the marginal effect of an additional ten dollars in monthly subsidies on the probability of coverage is approximately 6.5 percentage points, and decreases to roughly 2.5 percentage points above 200 percent FPL. Simulation results indicate that each 10 million dollars in annual state subsidies could retain roughly 5,000 enrollees, though the cost-effectiveness of these subsidies falls considerably once all enrollees below 200 percent of the federal poverty level are fully subsidized. We conclude that states are well positioned to mitigate, but not stop, coverage losses from expanded premium tax credit subsidy expiration.

Jae Youn Ahn, Hong Beng Lim, Mario V. Wüthrich

Integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) models are a popular framework for modeling serial dependence in count time-series. While convenient for modeling, prediction, and estimation, INGARCH models lack a clear theoretical justification for the evolution step. This limitation not only makes interpretation difficult and complicates the inclusion of covariates, but can also make the handling of missing data computationally burdensome. Consequently, applying such models in an insurance context, where covariates and missing observations are common, can be challenging. In this paper, we first introduce the marginalized state-space model (M-SSM), defined solely through the marginal distribution of the observations, and show that INGARCH models arise as special cases of this framework. The M-SSM formulation facilitates the natural incorporation of covariates and missing data mechanisms, and this representation in turn provides a coherent way to incorporate these elements within the INGARCH model as well. We then demonstrate that an M-SSM can admit an observation-driven state-space model (O-SSM) representation when suitable assumptions are imposed on the evolution of its conditional mean. This lifting from an M-SSM to an O-SSM provides a natural setting for establishing weak stationarity, even in the presence of heterogeneity and missing observations. The proposed ideas are illustrated through the Poisson and the Negative-Binomial INGARCH(1,1) models, highlighting their applicability in predictive analysis for insurance data.

Konstantin Kunze

This paper exploits variation resulting from a series of federal and state Medicaid expansions between 1977 and 2017 to estimate the effects of children's increased access to public health insurance on the labor market outcomes of their mothers. The results imply that the extended Medicaid eligibility of children leads to positive labor supply responses of single mothers and to negative labor supply responses of married mothers. The analysis of mechanisms suggests that extended children's Medicaid eligibility positively affects take-up of Medicaid and health of children.

Sarolta Tóth, Diána Kaszás, János Sónyák et al.

Abstract Loss of epithelial cell polarity and tissue disorganization are hallmarks of carcinogenesis, in which Ca2+ signaling plays a significant role. Here we demonstrate that the plasma membrane Ca2+ pump PMCA4 (ATP2B4) is downregulated in luminal breast cancer, and this is associated with shorter relapse-free survival in patients with luminal A and B1 subtype tumors. Using the MCF-7 breast cancer cell model we show that PMCA4 silencing results in the loss of cell polarity while a forced increase in PMCA4b expression induces cell polarization and promotes lumen formation. We identify Arf6 as a regulator of PMCA4b endocytic recycling essential for PMCA4-mediated lumen formation. Silencing of the single pmca gene in Drosophila melanogaster larval salivary gland destroys lumen morphology suggesting a conserved role of PMCAs in lumen morphogenesis. Our findings point to a role of PMCA4 in controlling epithelial cell polarity, and in the maintenance of normal glandular tissue architecture.

Margarita Echeverri, Kyazia Felder, David Anderson et al.

Background: African American men (AAM) have persistently had the highest incidence and mortality rates for prostate cancer (PrCa) in the United States. Considering that current guidelines recommend the practice of shared decision-making to determine whether or not to undergo a Prostate-Specific Antigen (PSA) test for the early detection of PrCa, this study focuses on the identification of key factors influencing AAM decisions regarding having or not having PSA screenings. Methods: Cross-sectional study of 152 AAM who completed study surveys. Statistical analysis included frequencies, means, and distributions and methods to test for differences in knowledge confidence, satisfaction and, self-efficacy when making decisions. Results: 94% of participants would get a PSA test if offered, only 57% knew that the PSA test is a blood test. Participants who reported having had a PSA test before the baseline had significantly higher mean scores than their counterparts in the knowledge about the definition of the PSA and biopsy exams (p = 0.04), and in the confidence (p = 0.005) and efficacy (p = 0.002) scales when making PSA screening decisions. Older participants were more likely to have had a PSA test (p < 0.0001) and to intend to screen (p = 0.0441). Conclusions: Significant differences in the satisfaction scale by clinical site (p < 0.001) may underscore the influence of clinicians’ practices in participants’ satisfaction with their decisions. Results suggest that patients’ experience of care has the potential to positively influence PSA screening. It is our call that type of health insurance, knowledge about PrCa and PSA, and having had a PSA test in the past, as well as the patient’s characteristics (age, race and family history of PrCa) be considered when discussing with patients the harms/benefits of PSA screening and their preferences to have or not have the PSA test.

Yifang Chen, Xinyi Zuo

Abstract Background There is growingevidence in Europe and America that Home- and community-based services (HCBSs) are widely used for older people (aged ≥ 60 years). To date, however, no published evidence has discussed the correlation between HCBSs and depressive symptoms in older adults in China. Objective This cross-sectional survey aimed toexamine the relationship between HCBSs and depressive symptoms in older people. In addition, this study explored the relationship between internet use and depression risk in the older population at the individual and provincial levels and in rural and urban subgroups. Methods The survey of CHARLS used a multistage probability-proportional-to-size (PPS) sampling technique. Province-level socioeconomic characteristics were merged with microdata for respondents over 60 years of age from the 2020 China Health and Retirement Longitudinal Study (CHARLS 2020) Wave 5 (n = 10,036) by the author. The respondents were asked whether they used the HCBSs. Depression was assessed using the 10-item Centre for Epidemiologic Studies Depression Scale(CES-D 10). The chi-square test was used to explore whether HCBSs were related to mental health status. A binary logistic regression model was used to determine this association further after controlling for confounding factors. Results Overall, 24.8% (2155/8692) of the participants used HCBSs. Regression analyses revealed that older adults who utilized HCBSs (OR = 0.486, 95% CI: 0.404–0.585, P < .001) had a lower risk of depressive symptoms than those who did not utilize HCBSs and older adults who were female (P = .001), single (P < .001), illiterate (P < .001), living in rural areas (P < .001), without retirement pensions (P < .001), with health insurance (P < .001) and with very poor Self-rated Health Index (SRH) (P < .001) had a greater risk of depressive symptoms. For province-level characteristics, older adults living in provinces with the number of beds in medical institutions per 10,000 persons in the second quartile were more likely to suffer from depressive symptoms, while there is no effect between depression and gross domestic product (GDP). Conclusions HCBSs had a positive effect on depression of older adults who lived in rural. However, the rate of HCBS usage remains low among older Chinese people. Therefore, the HCBS penetration rate should be a priority, especially in rural areas.

Zhengming Li, Jianxi Su, Maochao Xu et al.

The remarkable growth of digital assets, starting from the inception of Bitcoin in 2009 into a 1 trillion market in 2024, underscores the momentum behind disruptive technologies and the global appetite for digital assets. This paper develops a framework to enhance actuaries' understanding of the cyber risks associated with the developing digital asset ecosystem, as well as their measurement methods in the context of digital asset insurance. By integrating actuarial perspectives, we aim to enhance understanding and modeling of cyber risks at both the micro and systemic levels. The qualitative examination sheds light on blockchain technology and its associated risks, while our quantitative framework offers a rigorous approach to modeling cyber risks in digital asset insurance portfolios. This multifaceted approach serves three primary objectives: i) offer a clear and accessible education on the evolving digital asset ecosystem and the diverse spectrum of cyber risks it entails; ii) develop a scientifically rigorous framework for quantifying cyber risks in the digital asset ecosystem; iii) provide practical applications, including pricing strategies and tail risk management. Particularly, we develop frequency-severity models based on real loss data for pricing cyber risks in digit assets and utilize Monte Carlo simulation to estimate the tail risks, offering practical insights for risk management strategies. As digital assets continue to reshape finance, our work serves as a foundational step towards safeguarding the integrity and stability of this rapidly evolving landscape.

Eunice Mwangi, Wanja Tenambergen

Background: National Health Insurance Fund (NHIF) outpatient services operate under capitation, and are strategically designed to enhance quality, equity, and affordability. However, despite this strategy, providers often demand out-of-pocket payments. This study investigates provider participation in strategic purchasing and its impact on NHIF’s outpatient service delivery. Methods: A cross-sectional analytical study was conducted among 66 healthcare managers from facilities accredited to provide NHIF outpatient services in two counties in Kenya. The data were collected using structured questionnaires. Logistic regression analysis was performed to assess the association between study variables. Results: Most respondents were male (36, 55%). The provision of NHIF outpatient services was significantly correlated with the monitoring provider performance by NHIF and the county department of health (Pmultivariate=0.024). There was a 31-fold increase in the likelihood of provision for monitored facilities. A significant difference (p=0.005**) was observed in monitoring health facilities. Monitoring was more common among private healthcare providers than public and faith-based health facilities. Conclusion: The involvement of healthcare providers in strategic purchasing has not yet been achieved. County and NHIF quality assurance departments should regularly monitor providers’ performance to ensure the delivery of equitable and high-quality healthcare.

Min-Seok Kim, Youn-Hee Lim, Jongmin Oh et al.

Ozone concentrations have increased in recent decades, and several studies have reported that long-term exposure to ozone increases the mortality risk induced by respiratory conditions. However, research on cause-specific mortality related to ozone exposure and respiratory diseases remains scarce. We constructed a retrospective cohort of 5,360,032 adults aged ≥ 65 years from the National Health Insurance Service of Republic of Korea, and death certificates were obtained from Statistics Republic of Korea to determine the cause of death between 2010 and 2019. The daily maximum 8 h average levels of ozone during the warm season annually (May–September) and other air pollutants were determined for the residential district. We analyzed the data using a time-varying Cox proportional hazards model with individual- and district-level covariates, incorporating a competing risk framework to address deaths from causes other than chronic obstructive pulmonary disease (COPD) and asthma. In our single-pollutant model with a 3-year moving average, a 1 ppb increase in ozone exposure was associated with a hazard ratio (HR) of 1.011 (95% confidence interval [CI]: 1.008–1.013) for COPD mortality and an HR of 1.016 (95% CI: 1.011–1.022) for asthma mortality. In our model adjusted for the presence of underlying diseases and district-level variables, the HRs were 1.009 (95% CI: 1.008–1.014) for COPD and 1.017 (95% CI: 1.011–1.023) for asthma, respectively. These associations remained robust in our two-pollutant model, except for NO₂ and COPD. A linear concentration–response relationship was identified between ozone concentration, COPD, and asthma mortality. In this large nationwide cohort study, long-term exposure to ozone was associated with an increased risk of death from COPD and asthma in older Korean adults.

Christopher Krapu, Mark Borsuk

Data analysis and individual policy-level modeling for insurance involves handling large data sets with strong spatiotemporal correlations, non-Gaussian distributions, and complex hierarchical structures. In this research, we demonstrate that by utilizing gradient-based Markov chain Monte Carlo (MCMC) techniques accelerated by graphics processing units, the trade-off between complex model structures and scalability for inference is overcome at the million-record size. By implementing our model in NumPyro, we leverage its built-in MCMC capabilities to fit a model with multiple sophisticated components such as latent conditional autoregression and spline-based exposure adjustment, achieving an 8.8x speedup compared to CPU-based implementations. We apply this model to a case study of 2.6 million individual policy-level claim count records for automobile insurance from Brazil in 2011. We illustrate how this modeling approach significantly advances current risk assessment processes for numerous, closely related outcomes. The code and data are available at https://github.com/ckrapu/bayes-at-scale.

Mark Kelbert, Harold A. Moreno-Franco

We study the problem of optimal risk policies and dividend strategies for an insurance company operating under the constraint that the timing of shareholder payouts is governed by the arrival times of a Poisson process. Concurrently, risk control is continuously managed through proportional reinsurance. Our analysis confirms the optimality of a periodic-classical barrier strategy for maximizing the expected net present value until the first instance of bankruptcy across all admissible periodic-classical strategies.

Hansjoerg Albrecher, Pablo Azcue, Nora Muler

In this paper we study the problem of optimally paying out dividends from an insurance portfolio, when the criterion is to maximize the expected discounted dividends over the lifetime of the company and the portfolio contains claims due to natural catastrophes, modelled by a shot-noise Cox claim number process. The optimal value function of the resulting two-dimensional stochastic control problem is shown to be the smallest viscosity supersolution of a corresponding Hamilton-Jacobi-Bellman equation, and we prove that it can be uniformly approximated through a discretization of the space of the free surplus of the portfolio and the current claim intensity level. We implement the resulting numerical scheme to identify optimal dividend strategies for such a natural catastrophe insurer, and it is shown that the nature of the barrier and band strategies known from the classical models with constant Poisson claim intensity carry over in a certain way to this more general situation, leading to action and non-action regions for the dividend payments as a function of the current surplus and intensity level. We also discuss some interpretations in terms of upward potential for shareholders when including a catastrophe sector in the portfolio.

Marizol Lizbeth Serrano Quispe, Fred Torres Cruz

CATASTROAGRI is an application developed to load, analyze and interactively visualize relevant data on catastrophic agricultural insurance. It also focuses on the analysis of an ARIMA (0,1,1) (0,1,1) model to identify and estimate patterns in the agricultural data of the Puno Region, it presents a decreasing trend because there is a significant relationship between successive values of the time series, We can also state that it is not stationary because the mean and variance do not remain constant over time and the series has periods, and it is observed that the cases are decreasing and increasing over the years, especially the amount to indemnify due to the behavior of the climate in the highlands. The results of the analysis show that agricultural insurance plays an important role in protecting farmers against losses caused by adverse climatic events. The importance of concentrating resources and indemnities on the most affected crops and in the provinces with the highest agricultural production is emphasized. The results of the users' evaluation showed a high level of satisfaction, as well as ease of use.

Kremer, Sebastian, Konrad, Leon, Stroh, Max-Ferdinand et al.

Originating in 2011, Industry 4.0 describes the digital revolution of industry and has since become a collective term for smart, mutable and data driven factories. During the last decade systemic and methodical solutions were designed and implemented that enable corresponding data driven use cases for producers. Today's system providers offer complex data ecosystems in which data-driven use cases are built-in and implementers offer focused digitalisation projects to rapidly address quick wins. While an assessment of expectations around Industry 4.0 results in requirements within the domains of modifiability, connectivity, data and organisation for an IT-architecture, many such solutions are found to be violating essential requirements as systemic flexibility and data-availability. Not only is this a relevant matter for architectural purists, but it highlights real problems that industry is still facing while applying digitalisation measures in pursuit of Industry 4.0. While event-driven architectures go back to the design of modern operating systems, the emergence of powerful, resilient and cheap broker-technologies has risen the polarity of event-driven IT-architectures for businesses in the last decade. Although its occurrence is predominantly represented in ecommerce, finance and insurance, many prominent manufactures have since begun their transformation into an event-driven IT-architecture. Reasons for this architectural adaptation include exceptional data availability, resilience, scalability and especially data sovereignty. An assessment of event-driven IT-architecture's properties and implications reveals an excellent fit for the architectural requirements of Industry 4.0. In this work the subject of Industry 4.0 is analysed along literature to derive a collective understanding of expectations from a factory implementing Industry 4.0. Subsequently, IT-architectural requirements are derived that describe an architecture capable of satisfying these expectations. Then event-driven IT-architectures are analysed regarding their structural composition and capabilities. Finally, the fit of event-driven IT-architecture is evaluated against the architectural requirements of Industry 4.0, discussing congruence and divergence.

Jonas Crevecoeur, Katrien Antonio, Stijn Desmedt et al.

Due to the presence of reporting and settlement delay, claim data sets collected by non-life insurance companies are typically incomplete, facing right censored claim count and claim severity observations. Current practice in non-life insurance pricing tackles these right censored data via a two-step procedure. First, best estimates are computed for the number of claims that occurred in past exposure periods and the ultimate claim severities, using the incomplete, historical claim data. Second, pricing actuaries build predictive models to estimate technical, pure premiums for new contracts by treating these best estimates as actual observed outcomes, hereby neglecting their inherent uncertainty. We propose an alternative approach that brings valuable insights for both non-life pricing as well as reserving. As such we effectively bridge these two key actuarial tasks that have traditionally been discussed in silos. Hereto we develop a granular occurrence and development model for non-life claims that tackles reserving and at the same time resolves the inconsistency in traditional pricing techniques between actual observations and imputed best estimates. We illustrate our proposed model on an insurance as well as a reinsurance portfolio. The advantages of our proposed strategy are most compelling in the reinsurance illustration where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes.

Yunfei Ge, Quanyan Zhu

Supply chain security has become a growing concern in security risk analysis of the Internet of Things (IoT) systems. Their highly connected structures have significantly enlarged the attack surface, making it difficult to track the source of the risk posed by malicious or compromised suppliers. This chapter presents a system-scientific framework to study the accountability in IoT supply chains and provides a holistic risk analysis technologically and socio-economically. We develop stylized models and quantitative approaches to evaluate the accountability of the suppliers. Two case studies are used to illustrate accountability measures for scenarios with single and multiple agents. Finally, we present the contract design and cyber insurance as economic solutions to mitigate supply chain risks. They are incentive-compatible mechanisms that encourage truth-telling of the supplier and facilitate reliable accountability investigation for the buyer.

Umu Luluk Atin Lu’lu’il Maknuun, Moch. Ichiyak Ulumudin

Penelitian ini bertujuan menganalisa pengaruh prinsip 6C (Character, Capacity, Capital, Collateral, Condition, Constraint) dalam proses penilaian kelayakan calon debitur melalui keputusan pemberian pembiayaan griya terhadap tingkat pengembalian angsuran pada Bank Syariah Mandiri Area Surabaya Darmo. Penelitian ini menggunakan metode kuantitatif dengan analisa PLS. Hasil analisis data diperoleh bahwa character (X1) berpengaruh positif tidak signifikan terhadap keputusan pemberian pembiayaan (Z), Capital (X2) berpengaruh signifikan, Capacity (X3) berpengaruh negative tidak signifikan, Collateral (X4) berpengaruh positif tidak signifikan, Condition (X5) berpengaruh positif tidak signifikan dan Constraint (X6) berpengaruh negative tidak signifikan. Keputusan pemberian pembiayaan griya (Z) berpengaruh negative tidak signifikan terhadap tingkat pengembalian angsuran. Character (X1) berpengaruh signifikan terhadap tingkat pengembalian angsuran (Y), Capital (X2) berpengaruh signifikan terhadap (Y), Capacity (X3) berpengaruh negative tidak signifikan, Collateral (X4) berpengaruh signifikan, Condition (X5) berpengaruh negative tidak signifikan, Constraint (X6) berpengaruh positif tidak signifikan terhadap tingkat pengembalian angsuran (Y). Dari hasil analisa dapat disimpulkan bahwa prinsip 6C pada dasarnya kurang tepat digunakan dalam proses pengambilan keputusan pemberian pembiayaan griya sedangkan pada tingkat pengembalian angsuran prinsip 6C cukup mengakomodir.

Yu Li MD, Wen Li MD, Zhihui Wu MD et al.

Aim: This study aimed to investigate the influencing factors of the medical-seeking behavior of patients in a hospital in Nanning and descriptively analyze the main factors to further improve the medical system and optimize the allocation of health resources. Subject and methods: The willingness to seek medical treatment questionnaire survey was conducted on patients who were in the outpatient clinic of a hospital in Nanning from Jun 2018 to Aug 2019. The patients’ basic information was analyzed descriptively using the SPSS 23.0 software package, and the influencing factors of the willingness to seek medical treatment were analyzed by univariate analysis method. In addition, the importance of influencing factors in patient preference to seek medical treatment was explored by constructing a decision tree model. Results: A total of 3428 questionnaires were valid and the effective rate was 93.78%. Region, age, occupation, educational level, monthly income, insurance type, and disease type demonstrated diverse influences on the medical expenses of patients. In addition, differences were found between occupation and patient insurance situation, personal willingness to seek medical treatment, reasons for visiting the hospital, medical selection standard, preferred medical treatment location for common diseases, waiting time, treatment time, and manner of understanding the disease. Conclusion: Increasing attention has been paid on the patients’ preference for medical treatment and their satisfaction with medical services. Medical institutions should reasonably allocate the proportion of medical insurance reimbursement and diversify the registration and appointment methods. Patients should be treated in different periods and properly allocated to improve the service mechanism of primary medical institutions. In addition, it is necessary to improve the medical publicity model and the efficiency of medical services according to the needs of patients, so as to relieve the pressure of medical treatment in large general hospitals.