Hasil untuk "Insurance"

Noah Gbenga ALLI

This study examines the integration of green marketing strategies and product innovation within the Nigerian insurance sector. Through a quantitative survey of 247 insurance professionals, the research explores how environmental sustainability initiatives influence product development, market positioning, and organizational performance. The findings reveal a moderate but uneven adoption of green marketing, with insurers prioritizing operational efficiencies like digitalization over market-facing product innovations. Statistical analysis demonstrates a significant positive relationship between green marketing practices and product innovation, which in turn correlates with enhanced market and environmental performance, particularly in brand reputation and operational efficiency. However, the study identifies substantial barriers to broader implementation, including low customer awareness, limited willingness to pay for green products, resource constraints, and an underdeveloped regulatory framework. This research contributes to the literature on green marketing in developing economies by providing empirical evidence of its strategic value and identifying the critical constraints hindering the sustainability transition in Nigeria's insurance industry.

Ousmane Diop, El Hadji Ba, Gwenaëlle Maradan et al.

Abstract Background Although Senegal’s Universal Health Coverage (UHC) program has improved access to care, it has sometimes led to overuse of health services. Moreover, the program’s substantial financial debt to health facilities (HFs) has exposed them to organizational and financial problems, making it difficult to renew drug stocks and pay salaries on time. The multiple effects of the UHC program, both on individuals and on HFs, may discourage individuals from seeking for care in HFs. This study analyzed structural and individual factors associated with healthcare-seeking behavior in the context of Senegal’s UHC program. Methods We used data from six HFs in the mostly rural Fatick region that participated in two different two surveys: ANRS 12399 Soignants Sénégal and CMUtuelleS. We performed a multilevel logistic regression model to identify individual and structural factors influencing healthcare-seeking behavior. Results Among the 313 individuals included, 63.3% were female and median age was 52 years (IQR [41⎯63]). Half the participants (50.5%) sought health services after experiencing a health problem in the two months prior to the survey. HFs, which the National Agency managing the UHC program (33.3%) owed most to, were better equipped and staffed, but also the most negatively affected by UHC implementation. Despite this, individuals sought care in these facilities just as frequently as in others, suggesting that being better equipped and staffed helped them to be more resilient to the negative effects of the UHC program. Individuals were less likely to seek care in smaller HFs. Instead, they were more likely to seek care in HFs providing HIV and tuberculosis care. Voluntary (i.e., paying) members (vs. other members) of community-based health insurance organizations, and individuals with a chronic illness (vs. without), were more likely to seek care. Persons with a disability (vs. without) and those experiencing food insecurity (vs. no food insecurity) were less likely to seek care. Conclusion Both the financial support provided to individuals by the UHC program and structural characteristics of the healthcare system were associated with seeking healthcare. Taking greater account of these structural aspects when implementing UHC could enhance the program’s effectiveness and help achieve its objectives.

Christos Christodoulou-Volos

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare the performance of classical and block bootstrap procedures in residual risk. Diagnostic tests indicate that standardized residuals are dependent, contrary to the independent and identically distributed (<i>i.i.d.</i>) assumption of conventional FHS. Comparing the block and ordinary bootstrapping approaches, we find that block bootstrap produces wider, more conservative confidence intervals, particularly in extreme tails (e.g., 0.1% and 99.9% percentiles). The findings suggest that block bootstrapping can be employed as a correction instrument in risk modeling where the standard volatility filters do not work. The article highlights the necessity to account for remaining dependencies and offers practical recommendations for more robust tail risk estimation during volatile markets.

Elizabeth Dadzie, Wilfried Kuissi-Kamdem, Marcel Ndengo

This paper investigates a robust optimal consumption, investment, and reinsurance problem for an insurer with Epstein-Zin recursive preferences operating under model uncertainty. The insurer's surplus follows the diffusion approximation of the Cramér-Lundberg model, and the insurer can purchase proportional reinsurance. Model ambiguity is characterised by a class of equivalent probability measures, and the insurer, being ambiguity-averse, aims to maximise utility under the worst-case scenario. By solving the associated coupled forward-backward stochastic differential equation (FBSDE), we derive closed-form solutions for the optimal strategies and the value function. Our analysis reveals how ambiguity aversion, risk aversion, and the elasticity of intertemporal substitution (EIS) influence the optimal policies. Numerical experiments illustrate the effects of key parameters, showing that optimal consumption decreases with higher risk aversion and EIS, while investment and reinsurance strategies are co-dependent on both financial and insurance market parameters, even without correlation. This study provides a comprehensive framework for insurers to manage capital allocation and risk transfer under deep uncertainty.

Hibiki Ayabe, Kazushi Okamoto, Koki Karube et al.

Structural fireproof classification is vital for disaster risk assessment and insurance pricing in Japan. However, key building metadata such as construction year and structure type are often missing or outdated, particularly in the second-hand housing market. This study proposes a multi-task learning model that predicts these attributes from facade images. The model jointly estimates the construction year, building structure, and property type, from which the structural fireproof class - defined as H (non-fireproof), T (semi-fireproof), or M (fireproof) - is derived via a rule-based mapping based on official insurance criteria. We trained and evaluated the model using a large-scale dataset of Japanese residential images, applying rigorous filtering and deduplication. The model achieved high accuracy in construction-year regression and robust classification across imbalanced categories. Qualitative analyses show that it captures visual cues related to building age and materials. Our approach demonstrates the feasibility of scalable, interpretable, image-based risk-profiling systems, offering potential applications in insurance, urban planning, and disaster preparedness.

Bingzhen Geng, Yang Liu, Hongfu Wan

Systemic risk is receiving increasing attention in the insurance industry. In this paper, we propose a multi-dimensional Lévy process-based renewal risk model with heterogeneous insurance claims, where every dimension indicates a business line of an insurer. We use the systemic expected shortfall (SES) and marginal expected shortfall (MES) defined with a Value-at-Risk (VaR) target level as the measurement of systemic risk. Assuming that all the claim sizes are pairwise asymptotically independent (PAI), we derive asymptotic formulas for the tail probabilities of discounted aggregate claims and the total loss, which hold uniformly for all time horizons. We further obtain the asymptotics of the above systemic risk measures. The main technical issues involve the treatment of uniform convergence in the dynamic time setting. Finally, we perform a detailed Monte Carlo study to validate our asymptotics and analyze the impact and sensitivity of key parameters in the asymptotic expressions both analytically and numerically.

Meylca R. Zanoria, Kier Dutches D. Laurito, Gerly A. Alcantara et al.

Postpartum depression (PPD) is a debilitating mental condition that mothers from low socio-economic backgrounds are more susceptible to acquiring. The study examined the socio-economic barriers affecting mothers’ motivation to seek medical PPD treatment using a descriptive correlational research design. A purposive sampling technique was used to recruit eighteen (18) mothers aged 20 - 39 years old who scored below 20 on the Edinburgh Postnatal Depression Scale (EPDS). The study’s findings suggested that educational attainment and health care insurance significantly correlate with mothers’ perceived barriers. Regarding their access to maternal postpartum care, age, income, educational attainment, and health care insurance directly correlate to their seeking attitude. Finally, financial and social barriers significantly impact the mothers’ access to maternal postpartum care, while geographic factors show no direct correlation. The study recommended coming up with means to alleviate economic constraints and the stigma of PPD to increase mothers’ motivations in seeking medical PPD treatment. Furthermore, the output of this study is aimed at developing an educational and livelihood program to alleviate financial and social barriers for mothers seeking PPD treatment.

Yaoyao Wu, Hanqi Liao, Lei Fang et al.

In recent years, with the deepening of the reform of rural economic systems, the demand for disaster risk governance in land production and management is increasing, and it is urgent for the state to develop agricultural insurance to improve land production recovery capacity and ensure national food security. The study develops a quantitative model to determine the agricultural premium rate for each county in China based on disaster risk level in order to refine agricultural insurance. The results show that: (a) in terms of the disaster situation, most of northeast and central China, part of southwest, north, and northwest China are seriously affected; (b) regarding the integrated natural disaster risk level, there are 129 counties with extremely high disaster risk in China; (c) as for agricultural premium rates based on the integrated natural disaster risk index, some counties in Inner Mongolia, Shanxi, Liaoning, Jilin, Shandong, Anhui, Jiangxi, Zhejiang, Guangdong, Hubei, and Hunan Province had extremely high rates, out of a total of 63 counties. The above results reveal regional differences in disaster risk levels and premium rates between counties, providing a reference for improving the accuracy of agricultural premium rates. This contributes to the creation of security for further improving land production capacity and promoting the intensification and sustainable development of agricultural production.

Qian Zhao, Chudamani Poudyal

The classical Bühlmann credibility model has been widely applied to premium estimation for group insurance contracts and other insurance types. In this paper, we develop a robust Bühlmann credibility model using the winsorized version of loss data, also known as the winsorized mean (a robust alternative to the traditional individual mean). This approach assumes that the observed sample data come from a contaminated underlying model with a small percentage of contaminated sample data. This framework provides explicit formulas for the structural parameters in credibility estimation for scale-shape distribution families, location-scale distribution families, and their variants, commonly used in insurance risk modeling. Using the theory of \(L\)-estimators (different from the influence function approach), we derive the asymptotic properties of the proposed method and validate them through a comprehensive simulation study, comparing their performance to credibility based on the trimmed mean. By varying the winsorizing/trimming thresholds in several parametric models, we find that all structural parameters derived from the winsorized approach are less volatile than those from the trimmed approach. Using the winsorized mean as a robust risk measure can reduce the influence of parametric loss assumptions on credibility estimation. Additionally, we discuss non-parametric estimations in credibility. Finally, a numerical illustration from the Wisconsin Local Government Property Insurance Fund indicates that the proposed robust credibility approach mitigates the impact of model mis-specification and captures the risk behavior of loss data from a broader perspective.

Yinhuan Li, Tsz Chai Fung, Liang Peng et al.

In non-life insurance, it is essential to understand the serial dynamics and dependence structure of the longitudinal insurance data before using them. Existing actuarial literature primarily focuses on modeling, which typically assumes a lack of serial dynamics and a pre-specified dependence structure of claims across multiple years. To fill in the research gap, we develop two diagnostic tests, namely the serial dynamic test and correlation test, to assess the appropriateness of these assumptions and provide justifiable modeling directions. The tests involve the following ingredients: i) computing the change of the cross-sectional estimated parameters under a logistic regression model and the empirical residual correlations of the claim occurrence indicators across time, which serve as the indications to detect serial dynamics; ii) quantifying estimation uncertainty using the randomly weighted bootstrap approach; iii) developing asymptotic theories to construct proper test statistics. The proposed tests are examined by simulated data and applied to two non-life insurance datasets, revealing that the two datasets behave differently.

Florian Gach, Simon Hochgerner, Eva Kienbacher et al.

Existence and uniqueness of solutions to the multi-dimensional mean-field Libor market model (introduced by [7]) is shown. This is used as the basis for a numerical asset-liability management (ALM) model capable of calculating future discretionary benefits in accordance with Solvency~II regulation. This ALM model is complimented with aggregated life insurance data to perform a realistic numerical study. This yields numerical evidence for heuristic assumptions which allow to derive estimators of lower and upper bounds for future discretionary benefits. These estimators are applied to publicly available life insurance data.

Abdülkadir Şenkal

The Great Transformation, published in 1944 by Karl Polanyi, brought a new dimension to the relationship between market, state, and welfare. Polanyi considered the relation between markets and societies as a central feature of any social order; according to him, while the market destabilizes society, the commodification of labor, land, and money creates a reaction or “counter-movement.” For this reason, he describes market society as being a dominant principle for social organization. Social relations are embedded within the economic system instead of the economy being embedded in social relations. Polanyi also claims that market society is a political and social construct rather than a natural phenomenon. Yet, the rapid growth of government bureaucracy and interference in the private sphere has challenged many traditional notions related to the nature of capitalist society, especially since the 1940s. Therefore, the state plays an important role in both the establishment and regulation of the private market economy. This article proposes an interpretation, in the context of the contemporary welfare state based on Polanyi’s The Great Transformation, which discusses the distinction between market, welfare, and labor. The institutions, that once contributed to embedding the market economy within society, now play an important role in situations that have potential consequences for those seeking help from the welfare state.

Mika Ogawa, Terumi Ayuse, Toshiaki Fujisawa et al.

Abstract Background Dental phobia is covered by medical insurance; however, the diagnostic methods are not standardized in Japan. Therefore, the aim of this study was to investigate the methods and use of questionnaires for the diagnosis of dental phobia by Japanese dental practitioners specializing in special needs dentistry and dental anesthesiology. Methods We conducted an online survey to obtain information from the members of the Japanese Society for Disability and Oral Health (JSDH, n = 5134) and the Japanese Dental Society of Anesthesiology (JDSA, n = 2759). Response items included gender, qualification, affiliation type, methods of diagnosis and management of dental phobia, use of questionnaire, need to establish standardized diagnostic method for dental phobia, and others. The chi-squared test was used to compare answers between the three groups: JSDH only, JDSA only, and both JSDH and JDSA. Multiple logistic regression analysis was conducted to identify factors associated with the use of an assessment questionnaire. Results Data were obtained from 614 practitioners (JSDH only, n = 329; JDSA only, n = 195; both JSDH and JDSA: n = 90, response rate: 7.8% [614/7,893], men: n = 364 [58.5%]). Only 9.7% of practitioners used questionnaires to quantify the level of dental anxiety. The members of both JSDH and JDSA group used questionnaires more frequently than members of the JSDH only (19% and 7.1%, respectively; Bonferroni corrected p < 0.01). Most practitioners (89.1%) diagnosed dental phobia based on patient complaints of fear of treatment. Furthermore, majority of the participants (73.3%) felt the need to establish standardized diagnostic method for “dental phobia.” Multiple logistic regression analysis showed that membership of the JSDH only was negatively related (odds ratio [OR] 0.28, 95% confidence interval [CI] 0.13–0.60), and use of behavioral therapy was positively related (OR 2.34, 95% CI 1.18–4.84) to the use of a questionnaire. Conclusions The results of this study showed that the use of questionnaires was very low, patients’ subjective opinions were commonly used to diagnose dental phobia, and a standardized diagnostic criterion was thus needed among practitioners. Therefore, it is necessary to establish diagnostic criteria for dental phobia in line with the Japanese clinical system and to educate dentists about them.

Yan Bai, Yan Bai, Yan Bai et al.

Tetrastigma hemsleyanum Diels et Gilg is a folk herb in Zhejiang Province with anti-inflammatory, antineoplastic, and anti-oxidation effects. Given its pharmacological activity, T. hemsleyanum is known as New “Zhebawei” and included in the medical insurance system of Zhejiang and other provinces. Flavonoids are the most important components of T. hemsleyanum, and their contents are mainly regulated by ultraviolet (UV) radiation. In this study, the total flavonoid contents, flavonoid monomer contents, and flavonoid synthesis related enzyme activities (phenylalanine ammonia–lyase, chalcone synthase, and chalcone isomerase), anti-oxidant enzyme activities (catalase, peroxidase, and superoxide dismutase), and biochemical indicators (malondialdehyde, free amino acid, soluble protein, and soluble sugar) in the leaves (L) and root tubers (R) of T. hemsleyanum with UV treatments were determined. Three kinds of UV radiation (UV-A, UV-B, and UV-C) and six kinds of radiation durations (15 and 30 min, 1, 2, 3, and 5 h) were used. Appropriate doses of UV-B and UV-C radiation (30 min to 3 h) induced eustress, which contributed to the accumulation of flavonoids and improve protective enzyme system activities and bioactive compound contents. Especially, certain results were observed in several special structures of the flavonoid monomer: quercetin contents in L increased by nearly 20 times, isoquercitrin contents in R increased by nearly 34 times; most of flavonoids with glycoside content, such as quercitrin (19 times), baicalin (16 times), and apigenin-7G (13 times), increased multiple times. Compared with the CK group, the flavonoid synthase activities, anti-oxidant enzyme activities, and biochemical substance contents in L and R all increased with UV treatments. This study provides a theoretical foundation for regulating flavonoids by light factors and improving the quality of T. hemsleyanum in production and medical industries.

Volkan Tunali

Management of software development demands including bug or defect fixes and new feature or change requests is a crucial part of software maintenance. Failure to prioritize demands correctly might result in inefficient planning and use of resources as well as user or customer dissatisfaction. In order to overcome the difficulty and inefficiency of manual processing, many automated prioritization approaches were proposed in the literature. However, existing body of research generally focused on bug report repositories of open-source software, where textual bug descriptions are in English. Additionally, they proposed solutions to the problem using mostly classical text mining methods and machine learning (ML) algorithms. In this study, we first introduce a demand prioritization dataset in Turkish, which is composed of manually labeled demand records taken from the demand management system of a private insurance company in Turkey. Second, we propose several deep learning (DL) architectures to improve software development demand prioritization. Through an extensive experimentation, we compared the effectiveness of our DL architectures trained with several combinations of different optimizers and activation functions in order to reveal the best combination for demand prioritization in Turkish. We empirically show that DL models can achieve much higher accuracy than classical ML models even with a small amount of training data.

Daouda Diawara, Ladji Kane, Soumaila Dembele et al.

According to the Chinese Health Statistics Yearbook, in 2005, the number of traffic accidents was 187781 with total direct property losses of 103691.7 (10000 Yuan). This research aims to fill the gap in the literature by investigating the extreme claim sizes not only for the entire portfolio. This empirical study investigates the behavior of the upper tail of the claim size by class of policyholders.

Francis Duval, Jean-Philippe Boucher, Mathieu Pigeon

Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection to vehicles' trip summaries, we develop a method allowing to derive a "routine" and a "peculiarity" anomaly profile for each vehicle. To this end, anomaly detection algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared to the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared to trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve classification.

Emilie Sbidian, Myriam Mezzarobba, Jason Shourick et al.

Current management of moderate-to-severe psoriasis may be heterogeneous between European countries, probably due to differences in the organization of care. The aim of this study was to compare the utilization of systemic treatments for psoriasis between 2 coun­tries. All adults with psoriasis who were registered in the French (SNDS) and the Dutch (VEKTIS) national health insurance databases between 2012 and 2016 were eligible for inclusion. In France, 105,035 (15%) of 684,156 patients and, in the Netherlands, 37,405 (28.6%) of 130,822 patients received at least a systemic agent. In France, the proportion of patients treated with systemic agents was constant, while the type of drugs dispensed shifted from non-biological to biological agents. In the Netherlands, the first systemic treatment was methotrexate and, in France, acitretin. In France, the choice of the first biologic was much more variable than it was in the Netherlands, where a large proportion of patients were dispensed ustekinumab. This study highlights discrepancies between France and the Netherlands concerning the choice of first non-biologic agent and first biologic agent for patients with psoriasis. These discrepancies may be due to differences in the healthcare systems between the 2 countries.

Patrick Cheridito, John Ery, Mario V. Wüthrich

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. We illustrate the method on different examples from banking and insurance. We focus on value-at-risk and expected shortfall, but the approach also works for other risk measures.

Rukuang Huang

The classical Cramér-Lundberg risk process models the ruin probability of an insurance company experiencing an incoming cash flow - the premium income, and an outgoing cash flow - the claims. From a system's viewpoint, the web of insurance agents and risk objects can be represented by a bipartite network. In such a bipartite network setting, it has been shown that joint ruin of a group of agents may be avoided even if individual agents would experience ruin in the classical Cramér-Lundberg model. This paper describes and examines a phase transition phenomenon for these ruin probabilities.