Insurance Model Research Articles

Pháp luật về bảo hiểm xã hội đa tầng - Kinh nghiệm quốc tế và quy định mới tại Luật Bảo hiểm xã hội 2024

Multi-tier social security is a social insurance model designed at many levels, aiming to ensure comprehensive and sustainable social security suitable for different groups of subjects and levels of contribution in society. In Vietnam, the Law on Social Insurance 2024, passed by the 15th National Assembly on June 29, 2024, marks a significant step forward in developing a multi-tier social insurance system. Using the method of synthesizing materials and analyzing legal information, the article highlights experiences from various countries worldwide, including Thailand, China, and Sweden. It also focuses on identifying new features in the current legal regulations on multi-tiered social insurance in Vietnam, as reflected in the provisions of the 2024 Social Insurance Law concerning social pension benefits, basic social insurance regulations, and supplementary pension insurance. Based on this analysis, the article proposes several recommendations for competent state authorities to improve and enhance the effectiveness of implementing multi-tiered social insurance laws in Vietnam.

Social Health Insurance for Universal Health Coverage in Low and Middle-Income Countries (LMICs): a retrospective policy analysis of attainments, setbacks and equity implications of Kenya’s social health insurance model

ObjectivesTo analyse the potential of the Social Health Insurance (SHI) model to support the achievement of Universal Health Coverage (UHC) in Low and Middle-Income Countries (LMICs) through a policy analysis...

Risk attitudes and private disability insurance holdings

AbstractThis study identifies several factors that affect the demand for disability insurance (DI), such as wealth, health, financial literacy, and self-reported willingness to take financial risks, using a logit model and a random forest algorithm. While the role of risk aversion in insurance ownership is well established within theoretical insurance models, the empirical literature is still inconclusive. We provide evidence based on the Survey of Consumer Finances that willingness to take financial risks similar to wealth, health, and financial literacy is positively associated with the ownership of private DI. Furthermore, the literature has not explored multivariate risk attitudes in this framework. We thus employ indices of multivariate risk attitudes within a theoretical DI model. Our empirical results on private DI, in conjunction with the theoretical model we present, allow us to test hypotheses regarding multivariate household preferences and reject the hypothesis of correlation-neutral preferences on wealth and health.

INTERNATIONAL EXPERIENCE IN LEGAL REGULATION OF HEALTH INSURANCE: A COMPARATIVE ANALYSIS

Objective: This study aims to conduct a comprehensive comparative analysis of international health insurance systems, focusing on the legal regulation of health insurance to identify strengths and weaknesses. The goal is to develop evidence-based recommendations to improve Russia’s mandatory health insurance (MHI) system, addressing issues like funding, access to services, care quality, and legal frameworks. Method: The research applies a theoretical framework built on domestic and international scholarly works related to health insurance and insurance law. The study uses general scientific methods like analysis, synthesis, and comparison, complemented by comparative legal analysis and institutional review. Empirical data are drawn from legal acts, statistical data from organizations like WHO and ILO, and case studies. Results: The research identifies key health insurance models: state-funded (e.g., UK, Canada), social insurance (e.g., Germany, France), and private insurance (e.g., U.S.), each with distinct advantages and challenges. Global trends include an increasing role of the state in health insurance regulation and efforts to expand universal coverage. Countries are evolving from selective to universal health insurance systems, focusing on accessibility, affordability, and quality of care. Novelty: The study highlights the applicability of international models for enhancing Russia’s MHI system, focusing on expanding state regulation and ensuring universal coverage. Key recommendations include improving regulatory frameworks, addressing coverage gaps for specific groups like the self-employed and rural populations, and strengthening accountability among insurers and providers.

Ensuring health rights for Indonesian citizens overseas

This research examines BPJS Kesehatan as a mechanism for fulfilling the right to health, Indonesia's responsibilities in ensuring this right, and the realization of health rights for citizens abroad within the framework of human rights theory. Using a normative legal research method based on literature review, the study concludes that Indonesia’s social security system operates through a social insurance model aimed at protecting all Indonesian citizens under an insurance system to meet their basic health needs. While healthcare services have been prioritized, other critical health-related prerequisites—such as access to clean drinking water, adequate sanitation, sufficient nutrition, health information, environmental health, and workplace health—still require further attention. This highlights that the state’s efforts to fulfill health rights aim to enable citizens to lead a decent and mentally fulfilling life.

Labor Engine or Welfare Trap: New Evidence from the Impact of China's Long-Term Care Insurance Policies on Labor Market Performance

The long-term care insurance system is a crucial social service policy for disabled and elderly individuals with dementia in the context of the unfeasible traditional family old-age care model. Through the support of the social insurance model for old-age care security, it effectively addresses and meets the actual needs of long-term care recipients. This has become a significant way to alleviate the burden of disabled and demented elderly individuals, mitigate the impacts of ageing, and create employment opportunities for family caregivers. Using the long-term care insurance pilot policy implemented in Qingdao in 2012 as a quasi-natural experiment, this paper estimates the influence of long-term care insurance on the labor market performance of the pilot city using the synthetic control method of project policy evaluation. Several robustness tests of the labor market effects of long-term care insurance are conducted, utilizing two placebo methods based on time and space, and the ranked test method. The study's findings demonstrate that the implementation of the long-term care insurance policy pilot has a constructive influence on the employment choices of workers in the labor market. Specifically, it encourages participation in the workforce and expands the scale of labor force participation in the pilot cities. Furthermore, the policy's effect strengthens over time and has a marked positive effect on average employee wages, thereby spurring labor supply growth. Through further analysis, it can be determined that the pilot cities have contributed to an average annual growth of around 1 million people in total labor force participation between 2013-2017, surpassing the potential growth rate by approximately 10.5%. Additionally, during this period, the average employee wage displays a linear upward trend with a maximum increase of over 5,000 pounds per capita per year. This suggests a substantial effect on the overall performance of the labor market due to its growth-promoting influence. In addition, the analysis of the heterogeneity of its impact mechanism demonstrates that the long-term care insurance policy aims to enhance the willingness of tertiary industry workers to work and considerably increase the likelihood of their entry into the labor market. It also improves labor income expectations of urban private and self-employed workers, resulting in a virtuous circle of enhanced labor and employment driven by the increased value of labor and the strengthened willingness to participate.

Underwriting Decision Modeling Based on Regional Hazard Assessment and Urban Resilience

In recent years, frequent extreme weather disasters have posed serious challenges to the economic social, and post-disaster recovery capabilities of places such as the United States, and original insurance models and payout strategies are difficult to adapt. Scientific assessment and response to natural disaster risks have become a key topic. This paper presents the Urban Resilience Index (URI) derived from 18 indicators. The ability of the city itself is rationally assessed for disaster resilience, which better helps insurance companies predict the loss compensation caused by disasters. This study starts by analyzing the risk of disasters in various regions of the United States, takes the intensity and frequency of disasters and the toughness of the city's resistance to disasters into account, and uses nonlinear multi-objective optimization to derive the threshold value of the payout strategy in a certain region to be around 0.21 and 0.94, respectively, and at the same time, after the control of the scalar function of the number of iterations is between 10 and 15 times, the final determination of the insurance model that responds to the different regions and verifies. The insurance model to cope with other areas is finally determined and its feasibility is verified.

Hidden semi-Markov models for rainfall-related insurance claims

We analyze the temporal structure of a novel insurance dataset about home insurance claims related to rainfall-induced damage in Norway and employ a hidden semi-Markov model (HSMM) to capture the non-Gaussian nature and temporal dynamics of these claims. By examining a broad range of candidate sojourn and emission distributions and assessing the goodness-of-fit and commonly used risk measures of the corresponding HSMM, we identify an appropriate model for effectively representing insurance losses caused by rainfall-related incidents. Our findings highlight the importance of considering the temporal aspects of weather-related insurance claims and demonstrate that the proposed HSMM adeptly captures this feature. Moreover, the model estimates reveal a concerning trend: the risks associated with heavy rain in the context of home insurance have exhibited an upward trajectory between 2004 and 2020, aligning with the evidence of a changing climate. This insight has significant implications for insurance companies, providing them with valuable information for accurate and robust modeling in the face of climate uncertainties. By shedding light on the evolving risks related to heavy rain and their impact on home insurance, our study offers essential insights for insurance companies to adapt their strategies and effectively manage these emerging challenges. It underscores the necessity of incorporating climate change considerations into insurance models and emphasizes the importance of continuously monitoring and reassessing risk levels associated with rainfall-induced damage. Ultimately, our research contributes to the broader understanding of climate risk in the insurance industry and supports the development of resilient and sustainable insurance practices.

Funding deposit insurance

We present a quantitative model of deposit insurance to characterize the optimal levels of coverage for depositors and premiums raised from banks. Premiums contribute to a deposit insurance fund that lowers taxpayers’ resolution cost of bank failures. The key model tension is the policymaker’s dynamic tradeoff between building a fund to discourage moral hazard and insulate taxpayers from large fiscal shortfalls, and allowing banks to productively invest their deposits. We find that risk-adjusted premiums reduce moral hazard, enabling the policymaker to increase the share of covered deposits to total deposits by 12.5 percentage points and decrease the share of expected annual bank failures from 0.74% to 0.60%. The model predicts a fund-to-covered-deposits ratio that matches the data and declines in taxpayers’ income due to taxpayers’ risk aversion.

Strengthening Resilience and Sustainability for Post-Disaster Recovery: A Comparative Law and Economics Analysis on Smart Mixes Between Mechanisms

This article advocates smart mixes of mechanisms of post-disaster recovery in terms of boosting resilience and sustainability through liability rules, government intervention, insurance, and their combinations. Liability rules can provide compensation to victims and, in theory, also reduce disaster risks, while they have their limits in practice. The government widely intervenes in disaster compensation in many countries, but it faces challenges in how it can intervene fairly and efficiently, whether through informal channels, such as ad hoc charity, or structured approaches, such as compensation funds. As governments may struggle to provide efficient and effective compensation ex post, insurance may offer proactive solutions with models of first-party catastrophe insurance and third-party liability insurance ex ante. Where market failure, liability failure, and government failure may all arise, a smart mix of mechanisms is often preferable from a law and economic perspective, rather than relying solely on one framework. However, upon examining empirical evidence regarding the effectiveness of these instruments in China, the UK, France, Germany, and Turkey, it becomes apparent that a mix of mechanisms is not always applied in an effective manner. Models of mandatory comprehensive disaster insurance, and Public–Private Partnership (PPP), which both receive government intervention as a last resort to the market and liability failures, can reach the goal of more effective compensation for disaster victims, risk prevention and resilience when faced with disaster recovery, and should therefore be substantially implemented beyond the current levels. To be clear, the proposed mix of solutions mainly focuses on legal and economic dimensions which are rather limited, ignoring, for example, building codes for structures and physical interventions.

The state of the art and future of climate risk insurance modeling.

This study provides a comprehensive review of the literature on climate risk insurance modeling to identify lessons learned and knowledge gaps to be addressed by future research. These models are increasingly relevant due to the rising losses attributable to climate change. Insurance models estimate risk for different perils and simulate risk-related parameters for insurance schemes, such as premiums and deductibles. Most forward-looking models indicate that climate change and socioeconomic developments highly exacerbate future risk and increase insurance premiums. Various studies recommend charging risk-based premiums to incentivize adaptation efforts that limit this increase in climate risks. Other findings point toward introducing public-private insurance to cope with climate change and enhance risk spreading by introducing insurance purchase requirements or insurance products that cover multiple climate risks. Gaps that we identify in this literature include an underrepresentation of insurance assessments for developing countries and for hazards other than flooding. Additionally, we note a lack of research into insurance for non-agricultural commercial sectors. Furthermore, less than half of the studies take a forward-looking approach by incorporating climate change scenarios, and an even smaller percentage consider socioeconomic development scenarios. This limitation shows that current methods require additional development for assessing the effects of future climate risk on insurance. We recommend that future research develops such forward-looking models, considers using a more refined spatial scale, broadens geographical and hazard coverage, and includes the commercial sector.

Insurance analytics: prediction, explainability, and fairness

AbstractThe expanding application of advanced analytics in insurance has generated numerous opportunities, such as more accurate predictive modeling powered by machine learning and artificial intelligence (AI) methods, the utilization of novel and unstructured datasets, and the automation of key operations. Significant advances in these areas are being made through novel applications and adaptations of predictive modeling techniques for insurance purposes, while, concurrently, rapid advances in machine learning methods are being made outside of the insurance sector. However, these innovations also bring substantial challenges, particularly around the transparency, explanation, and fairness of complex algorithmic models and the economic and societal impacts of their adoption in decision-making. As insurance is a highly regulated industry, models may be required by regulators to be explainable, in order to enable analysis of the basis for decision making. Due to the societal importance of insurance, significant attention is being paid to ensuring that insurance models do not discriminate unfairly. In this special issue, we feature papers that explore key issues in insurance analytics, focusing on prediction, explainability, and fairness.

Blockchain and Machine Learning Integrated Secure Driver Behavior Centric Electric Vehicle Insurance Model

Blockchain and Machine Learning Integrated Secure Driver Behavior Centric Electric Vehicle Insurance Model

Different copula types and reliability applications for a new fisk probability model

In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.

A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping

The modern vehicle insurance industry is increasingly adopting Pay-As-You-Drive (PAYD) insurance models, aligning premium costs with driving behavior. Our study introduces a Bayesian approach to PAYD insurance, leveraging the strengths of Naive Bayes classifiers and Bayesian Networks to handle uncertainty and integrate prior knowledge in risk assessment. The Naive Bayes model achieved an 87.5% accuracy in predicting risk partitions. With the Bayesian Network providing insights into causal relationships through a Directed Acyclic Graph (DAG), we also address the challenges of traditional actuarial models — low interpretability of intra-factor relationships and thus hard to plan for risk management for both provider and policyholder. Our research contributes to optimizing insurance pricing strategies. Still, the causal mapping also dismisses the meaningfulness of using geographic grouping in insurance pricing (discriminatory or not). It reassures the theoretical advantage of the PAYD model over the traditional model, facilitating access to affordable coverage for policyholders.

Some bivariate Schur-constant distributions and application to life insurance

Schur-constant models play a particular role when modelling time in fields such as actuarial science, insurance, reliability and survival models. These models describe random lifetimes with a certain dependence. In this study, a relation between proportional hazard rate distributions and Schur-constant models is established. Bivariate Schur-constant models, whose marginals are proportional hazard rate distributed, are introduced. Then, the dependency analysis in life insurances is performed through Schur-constant and copula models. It is revealed that there are differences in pricing when individuals' future lifetimes are dependent.

Comparative analysis of forecasting models in the nonlife insurance: Insights from the SARIMA and ETS approaches

Comparative analysis of forecasting models in the nonlife insurance: Insights from the SARIMA and ETS approaches

Pricing models for small and micro loan portfolio insurance

From the perspective of the insurance deductible, this article constructs pricing models suitable for small and micro loan portfolio insurance under realistic conditions, and summarizes relevant pricing laws through examples. It is found that the option attribute of loan portfolio insurance contracts endowed by absolute or relative deductible clause can be used to price small and micro loan portfolio insurance. Meanwhile, owing to the offset effect of the loan portfolio on the credit risk of different single loans, the premium rate of loan portfolio insurance should be controlled between that of corresponding single loan insurance. Moreover, properly adjusting the deductible ratio, insurance period, and composition of the insured loan portfolio can create more space for reducing the insurance price of the loan portfolio or expanding insurance profits. Our study improves the pricing model system of small and micro loan insurance, which has important theoretical value and practical significance for the scientific pricing of loan portfolio insurance.

K-Medoids and Support Vector Machine in Predicting the Level og Building Damage in Earthquake Insurance Modeling

Yogyakarta, an Indonesian province prone to earthquakes, frequently suffers extensive damage to buildings, necessitating insurance coverage to mitigate potential losses. This study aims to forecast earthquake insurance premiums by predicting building damage levels resulting from earthquakes. Utilizing data from buildings affected by the June 30, 2023, earthquake in Yogyakarta, we employ K-Medoids Clustering and Support Vector Machine (SVM) to predict two categories of building damage: minor (labelled as 1) and heavy (labelled as 2). The total premiums for minor damage range from approximately USD 86.55 to USD 288.50, while for heavy damage, they range from USD 120.05 to USD 400.18 using the K-Medoids algorithm. Meanwhile, premiums for minor damage range from USD 83.14 to USD 277.13, and for heavy damage, they range from USD 223.67 to USD 745.55 using the SVM algorithm.

Сочетание страховых и бюджетных механизмов в пенсионном обеспечении военнослужащих и приравненных к ним лиц: дефекты российской модели пенсионного страхования

The article studies special aspects of double pension support of military servicemen and equalstatus persons from the standpoint of characteristic traits of the Russian pension insurance model and its fundamental insurance principles. The author pays attention to gaps in legal regulation of matters of mandatory pension insurance of military servicemen and equal-status persons and concludes that there is a need to eliminate the defects in laws on pension insurance of special subjects.