Hasil untuk "Insurance"

Ching-Lan Cheng, Y. Kao, Swu-Jane Lin et al.

R. C. Merton

M. Huggett

Glenn Hubbard, Jonathan S. Skinner, Stephen P. Zeldes et al.

Q. Meng, H. Fang, Xiaoyun Liu et al.

Bingzheng Chen, Jan Dhaene, Chun Liu et al.

This paper develops a dynamic equilibrium model of the insurance market that jointly characterizes insurers' underwriting, investment, recapitalization, and dividend policies under model uncertainty and financial frictions. Competitive insurers maximize shareholder value under a subjective worst-case probability measure, giving rise to liquidity-driven underwriting cycles and flight-to-quality behavior. While an equilibrium typically fails to exist in such dynamic liquidity management framework with external financial investment, we show that incorporating model uncertainty restores equilibrium existence under plausible parameter conditions. Moreover, the model uncovers a novel relationship between the correlation of insurance and financial market risks and the equilibrium insurance price: negative loadings may emerge when insurance gains and financial returns are positively correlated, contrary to conventional intuition.

Michel Denuit, Marie Michaelides, Julien Trufin

Autocalibration is known to be an important requirement for insurance premiums since it guarantees that premium income balances corresponding claims, on average, not only at portfolio level but also inside each group paying similar premiums. Also, fairness has become a major concern because unfair treatment may expose insurers to lawsuits or reputational damage. Translating fairness into conditional mean independence allows actuaries to combine autocalibration and fairness into the multicalibration concept. This paper studies the properties of multicalibration in an insurance context and proposes practical ways to implement it, through local regression or bias correction within groups including credibility adjustments. A case study based on motor insurance data illustrates the relevance of multicalibration in insurance pricing.

Bohan Li, Wenyuan Li, Kenneth Tsz Hin Ng et al.

A mutual insurance company (MIC) is a type of consumer cooperative owned by its policyholders. By purchasing insurance from an MIC, policyholders effectively become member-owners of the company and are entitled to a share of the surplus, which is determined by their own collective claims and premium contributions. This sharing mechanism creates an interactive environment in which individual insurance strategies are influenced by the actions of others. Given that mutual insurers account for nearly one-third of the global insurance market, the analysis of members' behavior under such a sharing mechanism is of both practical and theoretical importance. This article presents a first dynamic study of members' behavior in the prevalent mutual insurance market under the large-population limit. With members' wealth processes depending on the law of the insurance strategies, we model the surplus-sharing mechanism using an extended mean field game (MFG) framework and address the fundamental question of how strategic interactions in this setting influence individual decisions. Mathematically, we establish the global-in-time existence and uniqueness of the mean field forward-backward stochastic differential equation (MF-FBSDE) characterizing the Nash equilibrium strategy, employing techniques to accommodate realistic insurance constraints. Computationally, we develop a modified deep BSDE algorithm capable of solving the extended MFG problem with an additional fixed-point structure on the control. Utilizing this scheme, we examine how structural features of the MIC's design, such as the composition of risk classes and surplus-sharing proportions, reshape members' decisions and wealth through collective interactions, underscoring the central role of these mechanisms in MICs.

Jan Maelger

We develop a formalism for insurance profit optimisation for the in-force business constraint by regulatory and risk policy related requirements. This approach is applicable to Life, P&C and Reinsurance businesses and applies in all regulatory frameworks with a solvency requirement defined in the form of a solvency ratio, notably Solvency II and the Swiss Solvency Test. We identify the optimal asset allocation for profit maximisation within a pre-defined risk appetite and deduce the annual opportunity cost faced by the insurance company.

Philipp C. Hornung

The calculation of the insurance liabilities of a cohort of dependent individuals in general requires the solution of a high-dimensional system of coupled linear forward integro-differential equations, which is infeasible for a larger cohort. However, by using a mean-field approximation, the high dimensional system of linear forward equations can be replaced by a low-dimensional system of non-linear forward integro-differential equations. We show that, subject to certain regularity conditions, the insurance liability viewed as a (conditional) expectation of a functional of an underlying jump process converges to its mean-field approximation, as the number of individuals in the cohort goes to infinity. Examples from both life- and non-life insurance illuminate the practical importance of mean-field approximations.

Bo Wu

In order to explore whether environmental liability insurance has an important impact on industrial emission reduction, this paper selects provincial (city) level panel data from 2010 to 2020 and constructs a two-way fixed effect model to analyze the impact of environmental liability insurance on carbon emissions from both direct and indirect levels. The empirical analysis results show that: at the direct level, the development of environmental liability insurance has the effect of reducing industrial carbon emissions, and its effect is heterogeneous. At the indirect level, the role of environmental liability insurance is weaker in areas with developed financial industry and underdeveloped financial industry. Further heterogeneity analysis shows that in the industrial developed areas, the effect of environmental liability insurance on carbon emissions is more obvious. Based on this, countermeasures and suggestions are put forward from the aspects of expanding the coverage of environmental liability insurance, innovating the development of environmental liability insurance and improving the level of industrialization.

Shan Zhao, Shuo Kang

Abstract The current analysis aimed to evaluate the cost-effectiveness of benmelstobart plus anlotinib and chemotherapy for patients with ES-SCLC from the Chinese health-care system perspective. A mathematical decision model that simulated patients’ 3-week transition in 20-year time horizon was conducted to evaluate the economic value. Survival and safety data were gathered from ETER701 trial, cost and utility values were obtained from the local charges and previously published studies. Sensitivity and subgroup analyses were performed to examine the robustness of the model results and to support the health-decision making. For intention-to-treat (ITT) patients, benmelstobart plus anlotinib and carboplatin/etoposide could bring additional 0.60 and 0.71 QALYs with marginal cost of $91,424.86 and $98,504.86 compared with anlotinib plus carboplatin/etoposide and carboplatin plus etoposide, respectively, resulting in an incremental cost-effectiveness ratios (ICERs) were $153,444.29/QALY and $138,272.39/QALY, respectively, which were higher than the Chinese willingness-to-pay (WTP) threshold. Sensitivity analyses and subgroup analyses confirmed the robustness of the model results when parameters changed. Benmelstobart plus anlotinib and carboplatin/etoposide was unlikely to be the cost-effective first-line strategy compared with anlotinib plus carboplatin/etoposide and carboplatin plus etoposide for ES-SCLC patients in China. Reducing the price of benmelstobart could increase its cost-effectiveness.

Kembo M. Bwana, Evelyne F. Magambo

This study compares the performance of non-life insurance in Tanzania and Kenya. Specifically, the study compares the Technical and Scale Efficiency (TSE) of non-life insurance in the two countries. It further explores the sources of technical inefficiency in non-life insurance in Tanzania and Kenya. The study employs Data Envelopment Analysis (DEA) to estimate the efficiency of the non-life insurance firms by adopting two inputs: management expenses and commission paid, while premiums written, and net investment income were used as output variables. Data were extracted from the annual reports of non-life insurance companies in Tanzania and Kenya for the years 2014-2017 (four years). The study revealed that non-life insurance firms in Tanzania were more technical and scale efficient compared to their Kenya counterparts. When technical efficiency was further decomposed into pure and scale efficiency to examine what largely caused inefficiency, it was revealed that in both Tanzania and Kenya, inefficiency is largely derived from a lack of technical efficiency, which reflects issues of innovation in the sector, inappropriate management practices, operating at sub optimal size of operation and misallocation of resources in production system. This study offers information relevant for investors and policymakers to make informed decisions in the insurance sector in both countries. It further guides insurance firms on the important inputs and proper allocation of the resources in the production system. The findings also contribute to our growing understanding of the effectiveness of non-life insurance in the insurance markets of the two nations.

Vanessa Anggriawan, Ferry Jaya Permana, Benny Yong

Disasters that occur in Indonesia lead to financial loss. One approach to mitigating the financial impact is through the utilization of natural disaster insurance. Although natural disasters occur with a relatively small frequency, the associated losses are substantial. Insurance companies need to carefully consider the characteristics of natural disaster data, as these events can lead to significant claims and potentially result in the bankruptcy of insurance companies. Insurance companies can reduce the risk of bankruptcy by transferring some risk to reinsurance companies. In this paper, the disaster reinsurance premium is determined by considering both the mortality and economic risks using the peaks over threshold (POT) model under the standard deviation principle. The Poisson, generalized Pareto, and lognormal distributions are used to determine the premium, with parameters estimated using the maximum likelihood method. A simulation analysis is conducted using synthetic data generated with RStudio software, which includes the frequency of floods per year over 20 years, as well as the number of deaths and the number of houses damaged in each flood event. The threshold is determined using the percentage method, where 10% of the data is considered extreme values. The POT model is applied to various retention cases. The simulation results show that the risk of the number of damaged houses has a greater impact on the premium amount that the insurance company must pay to the reinsurance company than the risk of the number of deaths. Additionally, cases with retention values below the threshold result in the highest reinsurance premiums, while cases with retention values above the threshold result in the lowest reinsurance premiums. This paper also shows that the reinsurance premium changes almost linearly with the increase in the extreme value percentage. This study is among the first to apply the peaks over threshold model in combination with multiple distributions for reinsurance premium estimation in the Indonesian context. The findings provide new insights into the sensitivity of reinsurance premiums to damage thresholds and retention levels, offering a practical tool for insurers in disaster-prone regions.

Omer Atac, George C. Bryant, William B. Burrows et al.

Abstract Background The goal of this study was to examine referral and attendance patterns for diabetes self-management education and support (DSMES) among patients with both type 1 (T1D) and type 2 diabetes (T2D) at a regional medical center in Kentucky, and to identify demographic and clinical factors associated with these outcomes. Methods We analyzed electronic health records of adults with diabetes (n = 10,587; n = 817 with T1D and n = 9,770 with T2D) who received care from 1/1/2016-12/31/2019 at University of Kentucky HealthCare. We compared DSMES referral and attendance rates by various demographic and clinical factors and used logistic regression models to examine the association between these factors and DSMES referrals/attendance. Results DSMES referrals were made for 6.9% (n = 726) of our sample, and 40.2% (n = 292) of those referred attended DSMES. Referral rates were 11.6% for T1D and 6.5% for T2D. Attendance rates were 41.1% for T1D and 40.1% for T2D. Referrals was more common among females (OR 1.67, 95% CI 1.42–1.96), non-Hispanic Black and Hispanic individuals (OR 1.36, 95% CI 1.12–1.65 and OR 1.56, 95% CI 1.03–2.35), and less common among those aged 65+ (OR 0.31, 95% CI 0.20–0.48), those with public insurance (OR 0.75, 95% CI 0.63–0.88), and rural residents (OR 0.48, 95% CI 0.40–0.57). Patients with obesity (OR 1.42, 95% CI 1.18–1.71) and ≥ 9% A1C (OR 2.35, 95% CI 1.65–3.34) were also more likely to be referred. No factors were associated with DSMES attendance. Conclusions Despite clear guidelines recommending DSMES referrals for patients with diabetes, DSMES referral rates were low, and less than half of patients who were referred ultimately attended DSMES. Variation in referral rates across demographic and clinical characteristics highlights opportunities to improve and standardize referral processes.

Alireza Azarberahman, Mahmoodreza Mohammadnejadi Modi

PurposeThis research examines the structure of financial markets by integrating game theory and fuzzy logic. The objective is to develop a differential game model that analyzes competition among financial firms within a specific industry.Design/methodology/approachThis study employs a differential game model, where players set service prices, dynamically influencing market shares and profits over time. The model incorporates two fuzzy criteria—market power (price-variable cost ratio) and product differentiation (Herfindahl-Hirschman index)—to assess market structure. These criteria are applied to data from Tehran Stock Exchange (TSE) industries, specifically banking, insurance, and e-commerce, to evaluate their respective market structures.FindingsThe results indicate that financial industries tend to be closer to perfect competition compared to other market structures. Additionally, a comparative analysis of the status of these industries in relation to each other reveals that the banking and the e-commerce industries exhibit characteristics of monopolistic competition, whereas the insurance industry aligns more closely with perfect competition. This study provides useful insights into player behavior and its implications for financial policy, aiding in market analysis and forecasting.Originality/valueThis research offers a novel approach by integrating game theory and fuzzy logic to analyze the structure of financial markets.

Dorina Onoya, Idah Mokhele, Cornelius Nattey et al.

Background The ideal clinic realisation and maintenance (ICRM) programme in South Africa aims to elevate primary healthcare clinic (PHC) service quality in preparation for the National Health Insurance rollout. This study investigated ICRM implementation from clinic workers’ and patients’ experiences in the Gauteng province.Methods A mixed-methods cross-sectional survey was conducted across 45 Gauteng PHCs. Anonymous semistructured interviews with 335 clinic staff explored their knowledge and experiences with the ICRM programme. Facility assessments captured structural factors impacting ICRM implementation. Log-binomial regression was used to assess factors related to confidence in ICRM implementation and improvements in ICRM certification, and thematic analysis examined patient and staff experiences.Results While 86.9% of clinical/management staff (95% CI 75.1 to 93.6) reported understanding ICRM, only 41.9% (95% CI 32.6 to 53.0) could cite specific guidelines. Enablers included guideline training (reported by 47.9% of staff, 95% CI 39.6 to 56.3) and support from district teams (44.6%, 95% CI 36.6 to 52.8). Barriers included facility size (32.9%, 95% CI 25.7 to 40.9) and infrastructure challenges (28.4%, 95% CI 21.6 to 36.2). Staff confidence in ICRM implementation was moderate (63.1%, 95% CI 56.1 to 69.6), higher when ICRM champions were present (relative risk ratio (RRR) 2.3 vs not present, 95% CI 1.0 to 5.2), guidelines were clear to staff (RRR 2.3, 95% CI 1.1 to 5.0) and sufficient training was perceived (RRR 2.7, 95% CI 1.4 to 5.3). From 2018 to 2021, 60.3% of facilities (95% CI 43.8 to 74.8) improved in ICRM classification. Compared with facilities with no status change, staff from clinics with downgraded stats were less likely to report clear guidelines (RRR 0.5, 95% CI 0.2 to 1.0) to identify an ICRM champion (RRR 0.3, 95% CI 0.1 to 0.7) or have a knowledgeable manager (RRR 0.01, 95% CI 0.01 to 0.3).Conclusion Challenges in ICRM implementation persist. Staff knowledge, training and district support play significant roles, while clear guidelines, sufficient resources and effective leadership are essential for sustaining and enhancing ICRM performance.

Heeju Sohn

Dimitris Bertsimas, Cynthia Zeng

The escalating frequency and severity of natural disasters, exacerbated by climate change, underscore the critical role of insurance in facilitating recovery and promoting investments in risk reduction. This work introduces a novel Adaptive Robust Optimization (ARO) framework tailored for the calculation of catastrophe insurance premiums, with a case study applied to the United States National Flood Insurance Program (NFIP). To the best of our knowledge, it is the first time an ARO approach has been applied to for disaster insurance pricing. Our methodology is designed to protect against both historical and emerging risks, the latter predicted by machine learning models, thus directly incorporating amplified risks induced by climate change. Using the US flood insurance data as a case study, optimization models demonstrate effectiveness in covering losses and produce surpluses, with a smooth balance transition through parameter fine-tuning. Among tested optimization models, results show ARO models with conservative parameter values achieving low number of insolvent states with the least insurance premium charged. Overall, optimization frameworks offer versatility and generalizability, making it adaptable to a variety of natural disaster scenarios, such as wildfires, droughts, etc. This work not only advances the field of insurance premium modeling but also serves as a vital tool for policymakers and stakeholders in building resilience to the growing risks of natural catastrophes.