Search Results (556)

This study examines the relationship between environmental, social, and governance (ESG) criteria and profitability in the global insurance sector from two distinct perspectives. The System GMM analysis measures the associations between ESG criteria and asset profitability. The analysis, conducted using the CRADIS method and weighted by the CRISUS, MAXC, and NMV methods, determines the companies’ multidimensional performance rankings. Thus, the financial outcomes of companies’ sustainability investments are comprehensively revealed. According to the System GMM estimation results, environmental and social variables are negatively associated with asset profitability, whereas the governance variable and return on equity are positively associated with asset profitability. The leverage ratio and firm size are negatively associated with profitability. While asset profitability and return on equity stand out as the most significant factors compared with environmental, social, and governance variables, environmental and social variables have become increasingly prominent in decision-making processes since 2020. According to the NMV method, return on equity is the decisive criterion, whereas the CRISUS-MAXC integrated method identifies return on assets as the decisive criterion; in both methods, the leverage ratio remains variable and has the lowest impact. According to the CRADIS method rankings, Admiral Group and Zurich Insurance were identified as having the highest performance and the lowest volatility. CNA Financial, Great Eastern, and Hanwha Corp were identified as the lowest-performing companies. Sensitivity analysis results indicate that the NMV-CRADIS method is more resilient to changes in weights. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

The insurance industry is under increasing pressure to undergo digital transformation as global markets, customer demands, and regulatory requirements evolve. Despite this growing importance, the sector continues to face persistent obstacles that hinder progress. This study investigates the factors influencing the successful implementation of digitalization in South Africa’s insurance industry, focusing on technological, organizational, and environmental factors derived from the Technology–Organization–Environment (TOE) framework and Dynamic Capabilities Theory. Key challenges identified include legacy systems, inadequate IT infrastructure, limited software capabilities, high maintenance costs, resistance to change, poor communication, insufficient employee readiness, and a lack of coherent digital strategies. Using the Delphi method, data were collected from 16 experts, comprising senior executives from leading South African insurance companies and academics specializing in business management and digital transformation. Through two iterative rounds, the experts evaluated and achieved consensus on the critical barriers affecting digitalization within the sector. The results form the basis for a conceptual framework that links the TOE dimensions with dynamic capabilities, providing actionable guidance for overcoming organizational and technological barriers. The findings highlight the urgent need for innovative strategies that emphasize organizational readiness, leadership, change management, and regulatory alignment. A conceptual framework that integrates the technology–organization–environment (TOE) framework and Dynamic Capabilities Theory is proposed, offering a structured response to overcome these barriers. By linking theoretical perspectives with practical insights, this study enriches the understanding of digital transformation in the insurance sector and provides actionable guidance for policymakers and practitioners seeking to enhance competitiveness, resilience, and customer-centric innovation. Full article

Climate change poses significant challenges to the agricultural and financial sectors, affecting crop productivity and the overall financial stability. This study evaluates the robustness of the Actuaries Climate Index^TM (ACI), a relatively recent tool to measure the impact of climate change, by comparing its explanatory power to well-established weather-based indexes (WBIs) across two key sectors. In the agricultural context, the yields of three major crops are predicted using generalized statistical models and advanced machine learning algorithms with climate indexes as explanatory variables. To enhance model reliability and address multicollinearity among weather-related variables, the study also incorporates both principal component analysis and functional principal component analysis. A total of 22 models, each constructed with different sets of explanatory variables, illustrate the significant impact of wind speed and sea-level changes, alongside temperature and precipitation, on crop yield variability across six regions of the United States. For the financial market application, the analysis adapts the weather-derivative framework, as it is a critical instrument for energy companies, insurers, and agribusinesses seeking to hedge against weather-related risks. By analyzing the payoffs of derivative contracts that use WBIs and ACI components as underlying variables, the findings reveal that the ACI framework holds a strong potential as a comprehensive climate risk indicator, not only for the agricultural sector but also for the finance and insurance industries. Full article

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings. Full article

Background: Stroke remains a significant health and economic challenge both globally and in the Czech Republic. Although a structured network of specialized stroke centres exists, comparative data on patient outcomes and healthcare costs across hospital types are still lacking in the Czech context. This study analyzed real-world administrative data to assess 90-day mortality and healthcare costs after ischemic stroke, categorized by intervention and provider type. Methods: Claims data from six Czech health insurance companies, covering approximately 44% of the population, were used for the years 2017–2020. Patients aged 18 and older with a primary diagnosis of ischemic stroke (ICD-10 code I63) were included. Interventions were categorized as thrombectomy, thrombolysis, or other treatment, and providers were classified as comprehensive stroke centres (CSCs), primary stroke centres (PSCs), secondary referral hospitals (SRHs), or others. Costs were calculated from the payer perspective using administrative claims data, and standardized 90-day mortality and effective cost per survivor (ECPS) were computed. Funnel plots were used to evaluate provider variability in outcomes and costs. The analysis included 23,568 patients (47% female; mean age 70.6). Results: Thrombectomy was associated with the highest mean costs (€13,385), the highest 90-day mortality (29.3%), and the highest ECPS (€18,880). Patients receiving other treatments had the lowest costs (€2725) and lower mortality (14.4%). CSCs recorded the highest average costs (€5087) and mortality (16.7%), while SRHs had the lowest costs (€2204) and mortality (13.7%). Funnel plots revealed greater variability in costs, mainly driven by primary hospitalization, while mortality rates showed less variation. Conclusions: These findings suggest that while stroke outcomes are relatively consistent across providers, costs differ, possibly reflecting efficiency differences and case-mix severity. The study is limited by the lack of clinical severity data, highlighting the need to link administrative data with clinical registries for more comprehensive future evaluations. Full article

As large scale digitization continues to reform business processes, one critical challenge organizations are currently facing is managing the staggering amount of data flowing. Further, with large datasets comes the added complexity of insuring a cyber secure environment and shielding the information security management system (ISMS) from undesirable manipulations. Today’s drastic rise of cyberattacks urges the need for effective security frameworks to guard against unauthorized access and malicious acts impeding business operations. The latter of which compelled organizations to adopt holistic information security approaches, commonly implemented via ISMS frameworks. Further, to maintain an effective ISMS, ongoing monitoring and measurements are highly required. Considering the aforementioned points, this paper explores how organizations measure the effectiveness of their ISMS focusing on key performance indicators, metrics, and foundational components involved in information security management by categorizing metrics into governance, risk, and incident response as well as determining the maturity level based on ISO alignment, the presence, specificity and automation of KPIs. Based on empirical interviews with eight diverse organizations, the research findings reveal a wide range of maturity among organizations, from those lacking clear defined KPIs to those with sophisticated multi-layered systems. While special attention is paid to incident-response management, companies with a strong ISMS stand out because they use automated and proactive metrics for strategic reporting, whereas companies with a weaker ISMS often do not have organized KPIs and depend on random manual audits. Based on these results, the present work suggests an analysis framework for evaluating ISMS effectiveness. While previous studies have struggled to define clear ISMS measurement practices, this paper aims to provide insights on measurements by identifying the core building blocks of ISMS and revealing how they are evaluated to drive continual ISMS improvement. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

In the classical non-life insurance models, the capital reserve of an insurance company increases at a constant rate and decreases by downward jumps. We consider a generalization of this model by supposing that a fixed portion of the capital reserve is continuously invested in a risky asset whose price follows a geometric Brownian motion, while the complementary part is placed in a bank account with a constant rate of return. The quantity of interest is the ruin probability on the infinite time horizon as a function of the initial capital. In the present note, we assume only the continuity of the distribution of claims together with a standard moment restriction called “light tails.” Our main contribution is that we reveal, under such “minimalistic” hypotheses, that the ruin probability is smooth and satisfies a second-order integro-differential equation in the classical sense. We obtain the exact asymptotics for large values of the initial capital with “computable” constants and present results of numerical experiments. In contrast with other methods used in the theory, we rely upon only standard mathematics, allowing implementation in lecture courses for master’s students. Full article

Although the expected future impacts of climate change on the construction sector are significant and commonly accepted, the prospect and viability of contractual solutions to mitigate such effects lack investigation. Scenario planning enables leaders to prepare for the future by revealing the impending opportunities and threats to businesses and markets. This article offers analysis, synthesis, and evaluation of published literature and results from a scenario-planning workshop. The study reveals that climate change and profit margins are the main forces that will impact the construction sector in 2030. Evidential materials, contract provisions, and data repositories involving existing and emerging technologies are the three tenets of an innovative conceptual solution that can reduce delay and disruption disputes. This is significant because, inter alia, as the consequences of climate change are likely to increase, contract terms that allocate risks associated with it are likely to be modified, and insurance companies are liable to increase indemnification premiums, or become unable to cover such risks. The offered solution, namely the Trilateral Model, increases the sustainability of construction contracting in this context through a clear, impartial, acceptable, and effective risk allocation mechanism that mitigates the impact of those forces and offers contractual certainty. Full article

Pet insurance is a financial tool meant to provide financial support to pet owners for veterinary expenses. It was introduced to the Malaysian market in 2010 with several companies offering various subscription packages. However, public awareness of pet insurance has been low compared to other pet-related industries in Malaysia for the past five years. There is also a lack of research on the acceptance and adoption of pet insurance by pet owners in Malaysia. Therefore, this study aims to (i) assess the knowledge, attitudes, and practices (KAPs) of pet owners regarding pet insurance in Selangor, Malaysia, (ii) determine the associations of sociodemographic factors with the KAP levels, and (iii) determine the correlation between the levels of KAPs. A cross-sectional survey was conducted in Selangor, Malaysia, and 116 pet owners participated. Data were subjected to descriptive analysis statistics, followed by Chi-square tests of association and a Spearman correlation analysis, which were all performed using IBM SPSS Statistic version 27.0. The results showed that most of the respondents have poor knowledge of (n = 63, 54.3%), moderate attitudes towards (n = 63, 54.3%), and poor practices regarding (n = 87, 75.0%) pet insurance. Significant associations were indicated between income range per month and knowledge and practice (p < 0.05), highest education level and attitude (p < 0.05), and locality of residence and practice (p < 0.05). There is a significant correlation between total knowledge and practice scores (r = 0.649). The poor knowledge of insurance among pet owners in Selangor indicated the need to further increase the awareness of pet owners regarding the benefits of pet insurance through enhanced marketing and public education initiatives. Full article

Lebanon does not have a national mortality table that reflects its demographic and health conditions. Despite ongoing changes in mortality patterns driven by economic crises, political instability, and social changes, outdated foreign tables such as AM80 remain in use in the insurance and public sectors. This dependency introduces significant risks in actuarial calculations, policy design, and long-term planning. This study addresses this gap by building a mortality table specifically adapted to the Lebanese insurance context, together with a first estimation of population-level mortality. In the absence of any official mortality database, we collaborated directly with local insurance companies to access and organize internal records of insured lives. These data, which represent one of the few available structured sources of mortality information in the country, form the core of our analysis. We apply actuarial methods to estimate age-specific death rates and life expectancy and benchmark the results against national and international references to assess consistency and range. By offering a locally grounded, data-driven alternative to imported mortality assumptions, this work fills a critical statistical need. The resulting table supports more accurate forecasting, pricing, and demographic modeling, with applications across insurance, pensions, and public health planning in Lebanon. Full article

Pet health insurance can reduce the financial burden of veterinary care and ensure adequate treatment for companion animals. In France, where 67% of households own at least one pet and 68% of owners consider them family members, awareness of pet insurance reaches 94%, yet only around 5–6% of pets are insured. This review aims to provide an overview of the French pet health insurance market, analysing its structure, coverage options, and potential implications for veterinary practice. A literature review was conducted using French and English sources, complemented by simulated price quotes from major insurance companies for four virtual pets (two dogs and two cats). The analysis compared 11 major French pet insurance providers across criteria such as species covered, waiting periods, age limits, coverage rates, reimbursement mechanisms, and preventive care benefits. The results reveal significant variability in coverage options, preventive care allowances, and reimbursement procedures. Across providers, simulated annual premiums for the virtual pets ranged from EUR 71.76 to EUR 1426.44, with reimbursement rates of 50–100% and annual caps of EUR 763–2500. It can be concluded that pet insurance may help owners manage unexpected veterinary costs and encourage preventive care. However, subscription rates remain low due to limited understanding of insurance plans and perceived high costs. Wider adoption of pet insurance could improve access to care and ensure fair remuneration for veterinarians. Full article

In this study, we examine the impact of government green subsidies on corporate ESG performance. We employ the method of double machine learning for causal inference. We use all A-share listed companies in China from 2013 to 2023 as the research sample. After excluding financial and insurance companies, those in ST/*ST/PT status, and those with missing key indicators, we ultimately obtain 2337 sample observations. Our baseline results based on double machine learning reveal government green subsidies significantly enhance corporate ESG performance. The findings suggest that this enhancement occurs notably through the mediating variables of digital technology innovation and technology conversion efficiency. We also introduce heterogeneous dimensions such as the level of digital inclusive finance, the intensity of environmental regulations, and the scale of enterprises. Meanwhile, we adopt multiple robustness test methods, including changing the dependent variable, excluding data from special years, controlling for exogenous policy shocks, using instrumental variable methods, and resetting the double machine learning model—adjusting the sample partition ratio from the original 1:4 to 1:9 and replacing the prediction algorithm from random forest to gradient boosting, lasso regression, and ensemble machine learning methods—to ensure the reliability and scientific nature of the research conclusions. Additional tests indicate that the regression coefficient remains positive and is significant, indicating the robustness of our conclusions. This research offers implications for further optimizing the design of government green subsidy policies, and to promote the improvement of enterprises’ ESG performance and economic green transformation. Full article

The scope of this topic stems from the change in insurance companies and the subsequent transition to IFRS 17. The new code came into force on 1 January 2023. Therefore, the purpose of this article is to compare the two standards in terms of methodology and process logic. To highlight the new aspects of the new standard and to present the author’s view that IFRS 17 provides more opportunities for timely action and intervention by company management in the processes and improvement of results compared to IFRS 4. To examine how the application of the standard has affected the strategy for recognising, measuring, and reporting liabilities under insurance contracts, as well as financial results in the insurance sector in China. The study uses a mixed approach, combining a comparison of IFRS 4 and IFRS 17 with examples illustrating actual practice in the sector to examine differences in accounting treatment. It cites examples from European and Asian traders to assess how things will develop in practice. Contribution: This study adds new evidence on the impact of IFRS 17 on value and profit management. Our study found that the new standard introduces a single model for measuring insurance contracts, which significantly increases transparency and comparability in financial statements. Furthermore, one of its most important findings is that, with the equalisation of the margin on contractual services and the recognition of profits over the entire term of insurance contracts, the balance sheets for all years will show more consistent reports of profits and losses. It also calls for attention to the challenges insurers met in developing cash flow discounting methods or putting the general measurement model into effect. Overall, the report found that search engine IFRS 17 has made comparability and transparency better while making suggestions to industry stakeholders about what problems came out when they were discovered afterwards. Full article