Type of the article: Research ArticleAbstractThis study examines how patients’ health insurance claims were denied by different insurance providers at a Saudi Academic Medical Center (AMC), exploring the reasons for these rejections, their relationship to claim characteristics, and the factors that predict health insurance claim rejections. A descriptive study design was employed, involving a retrospective review of all insurance claims submitted by both inpatients and outpatients between January and December 2023 at a tertiary care AMC in Saudi Arabia. Following data screening using the UCAF 2.0 form, all denied insurance claims cases (n = 1,117) were subjected to qualitative analysis. The majority of rejected health insurance claims were submitted by female patients (56.9%) and outpatients (93.6%). Among the insurance companies studied, “Tawuniya” rejects the most insurance claims (n = 730). Variables such as age, gender, and insurance company were significantly associated with the reasons for denying claims (p < 0.05). Furthermore, variables such as age, cost, department type (inpatient/outpatient), and the month of claims are significant predictors of claim rejections (p < 0.05). However, gender, insurance companies, and clinical diagnosis were not significant (p > 0.05). The primary reasons for insurance claim denials in Saudi Arabia are missing medical data, system errors, and non-coverage of specific conditions. This study will help insurance companies and patients identify trends and reasons for claim rejections, enabling them to implement more effective preventive and corrective measures.AcknowledgmentsThe authors expressed their gratitude to Imam Abdulrahman Bin Faisal University for granting permission [IRB-2024-03-188] to conduct this study.

Type of the article: Research ArticleAbstractThe study aims to explore the insurance profiles of SMEs and to identify access gaps across different firm categories, using the Ipsos European Public Affairs survey dataset, which consists of 8,187 SMEs from Europe. This dataset was analyzed using multiple correspondence analysis, cluster analysis, and discriminant analysis. Results show clear evidence of ownership concentration in a few insurance products: commercial motor (64.3% of SMEs own such an insurance), general liability (54.4%), and workers’ compensation (46.1%). On the other hand, uptake is lowest for cyber insurance (15.3%), non-damage business interruption (14.5%), and commercial insurance with business interruption (20.3%); notably, 8.2% report no insurance product ownership. Furthermore, three behavioral clusters were identified: minimally insured (n = 3,604; mean 1.58 policies), moderately insured (n = 2,603; mean 5.22), and broadly insured (n = 1,980; mean 6.73). Also, portfolios exhibit structured “baskets” with frequent co-ownership of general liability and motor (40%). Findings document systematic, demography-linked disparities and actionable access gaps. The study concludes that persistent disparities in access are linked to firm size, age, and turnover, underscoring the need for tailored policy measures and market solutions to address inclusion gaps. The practical value of this study lies in providing evidence-based insights for insurers, regulators, and policymakers seeking to expand SME risk protection.AcknowledgmentWe gratefully acknowledge EIOPA for providing access to the SME insurance dataset used in this study. 

Type of the article: Research ArticleAbstractThe increasing complexity of insurance fraud in Jordan has unveiled inadequacies of traditional detection mechanisms, calling for advanced technologies. This study investigates drivers and inhibitors of machine learning adoption for fraud detection within Jordan’s insurance sector, with a focus on institutional readiness, ethical concerns, and supporting regulations. By applying quantitative and exploratory research design, Partial Least Squares Structural Equation Modeling serves as an approach to analyze data collected from 291 practitioners of fraud detection, data science, and insurance compliance in the industry.Findings show that both existing fraud detection efforts (coefficient = 0.42, p = 0.012) and knowledge of machine learning (coefficient = 0.55, p = 0.009) have favorable impacts on adoption likelihood, which underlines the relevance of bureau experience and informed professional culture. By contrast, major adoption deterrents such as limited IT capability, budgetary constraints, and moral concerns about fairness and clarity (coefficient = –0.40 and –0.38, respectively) unfavorably decrease adoption intention.Regulatory encouragement has a two-fold role: it has a direct promoting effect on adoption (coefficient = 0.47, p = 0.011) and a buffering effect on negative ethical concerns (interaction = 0.36, p = 0.025) and adoption barriers (interaction = –0.28, p = 0.032). Perceived efficacy also mediates between awareness/experience on the one hand and adoption decisions on the other (coefficients = 0.51 and 0.44, p < 0.05).The results demonstrate successful incorporation of machine learning into fraud detection as depending on the clarity of regulations, ethical protections, and institutional readiness, rather than on technical capability itself.

Type of the article: Research ArticleAbstractThis study investigates the key factors that shape individuals’ decisions to repurchase voluntary personal auto insurance, with particular attention to the mediating effect of customer satisfaction and the moderating role of perceived risk. Drawing upon the theory of planned behavior, perceived risk theory, and expectation-confirmation theory, the study employs a structured face-to-face survey conducted between January and April 2025 among 496 voluntary personal auto insurance policyholders in the Southeast region of Vietnam. Respondents were randomly selected from customers who had renewed their policies at least once, ensuring that the sample represented active policyholders with actual repurchase experience. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the hypothesized relationships. The analysis reveals that customer satisfaction is a critical driver of repurchase intention, acting both independently and through mediators such as brand image, subjective knowledge, and perceived value. Additional factors, namely price sensitivity and perceived behavioral control, also show positive effects on repurchasing behavior. Interestingly, perceived service quality does not significantly influence repurchase intention, indicating a potential shift in consumer expectations within digital insurance environments. Furthermore, the study finds that perceived risk mitigates the strength of the satisfaction-repurchase link, suggesting that even satisfied clients may hesitate to renew when uncertainty is high. The results contribute to theoretical models of post-purchase behavior and provide practical implications for insurers seeking to enhance customer loyalty through improved satisfaction, trust, and risk communication.AcknowledgmentThis research was conducted as part of the doctoral dissertation approved by Decision No. 1218/QD-DHNCT on December 14, 2023, by Nam Can Tho University, Vietnam. The authors express their gratitude to the reviewers and editor-in-chief for their valuable assistance in preparing this study.

Type of the article: Research ArticleAbstractThis paper examines the influence of reinsurance strategies and insurance liabilities on the performance and market valuation of Jordanian insurance firms. Using panel data from 2010 to 2023 and employing fixed-effects regression and mediation analysis, we test whether Excess Loss Installments (ELI) mediate these relationships. Based on a balanced panel of 16 listed Jordanian insurers over the period 2010–2023, the study applies SPSS, EViews, and SmartPLS to conduct fixed-effects regression and mediation analysis. The findings reveal that a higher reinsurers’ share is significantly associated with lower return on assets (ROA) (β = –0.18, p < 0.05), suggesting that excessive risk cession may erode underwriting profitability. In contrast, insurance contract liabilities have a strong positive impact on ROA (β = 0.29, p < 0.01) and firm value measured by Tobin’s Q (β = 0.32, p < 0.01), indicating that prudent technical reserve accumulation enhances financial strength and investor perception. Correlation analysis further revealed a negative association between reinsurance share and ROA (r = –0.21), while liabilities showed a moderate positive correlation with Tobin’s Q (r = 0.36). Mediation analysis showed that ELI does not play a statistically significant mediating role in the relationship between the main variables. In some models, ELI even had a minor negative indirect effect on firm value.These findings emphasize the importance of optimizing reinsurance structures and liability management. For Jordanian insurers, effective risk transfer must be balanced against profitability goals. Regulators and firm managers should revisit the strategic use of advanced mechanisms like ELI to reduce inefficiencies and strengthen financial outcomes.Acknowledgment(s)This research was funded through the annual funding track by the Deanship of Scientific Research, from the vice presidency for graduate studies and scientific research, King Faisal University, Saudi Arabia [Grant no. KFU253235].

Type of the article: Research ArticleAbstract Business intelligence systems are becoming vital in Jordan’s insurance sector, driving efficiency, compliance, and data-driven decisions. This study investigates how institutional, technical, and cultural conditions influence the effectiveness of business intelligence implementation, especially in overcoming persistent data silos and fragmented legacy systems. A structured survey was conducted between September and December 2024 across major Jordanian cities, targeting BI managers, IT specialists, compliance officers, and operations analysts within insurance companies. A stratified sampling approach was used to ensure representation by firm size, BI maturity, and data silo severity, yielding 260 valid responses from 360 distributed questionnaires (72% response rate). This focus on professionals directly involved in BI implementation and evaluation ensured the relevance and depth of insights.Partial Least Squares Structural Equation Modeling revealed that BI integration significantly reduced data silos (β = –0.482, p < 0.0001), improved operational efficiency (β = 0.413, p = 0.0003), strengthened regulatory compliance (β = 0.391, p = 0.0005), and enhanced decision-making effectiveness (β = 0.428, p < 0.0002). Mediation analysis confirmed that improved data quality partially explained BI’s impact on decision-making (β = 0.216, p = 0.0012). Moreover, the positive effects of BI were amplified in organizations with strong data-driven cultures (β = 0.183, p = 0.0026) and active top management support (β = 0.194, p = 0.0021). These findings underscore that technological solutions alone are insufficient; effective BI outcomes rely on an alignment of systems, culture, and leadership, offering critical insights for digital transformation in regulated industries.

Type of the article: Research ArticleAbstractThis study examines the influence of socio-economic factors on life insurance demand in Bangladesh using annual data from 18 life insurance companies between 2014 and 2023. Life insurance demand is assessed using life insurance penetration and life insurance density; GDP per capita, inflation, healthcare spending to GDP, and education spending to GDP serve as proxies for socio-economic variables. This study employs a dynamic Panel-Corrected Standard Errors (PCSE) method to handle cross-sectional dependence in panel data. Stepwise regression is further applied as a robustness check. The findings exhibit that GDP per capita has a statistically significant negative impact on insurance density (β = –0.0003, P < 0.001) and insurance penetration (β = –0.000002, P < 0.001). This suggests that income growth does not facilitate increased insurance adoption. In contrast, inflation has a significant positive influence on both insurance density (β = 0.0310, P < 0.001) and insurance penetration (β = 0.0001, P < 0.001), emphasizing the influence of inflationary pressure on life insurance demand. Similarly, healthcare expenditure exhibits a significant positive effect on life insurance demand, influencing both insurance density (β = 2.0560, P < 0.01) and insurance penetration (β = 0.0024, P < 0.05), possibly due to rising healthcare costs prompting individuals to seek financial security. However, education spending does not show a statistically significant effect on life insurance demand. The results indicate that demand for life insurance in Bangladesh is influenced more by financial insecurity than by income increases, emphasizing the impact of inflation and healthcare expenses on insurance adoption.

Type of the article: Research Article AbstractThe study aims to evaluate the impact of organizational support, customer awareness, perceived security, and regulatory compliance on e-commerce adoption within the insurance sector of Jordan. A structured questionnaire was administered to 400 participants from executive management, IT, customer service, and compliance departments who worked in ten Amman-based insurance companies. Believing that a quantitative research design matched the analysis requirements, 372 valid responses were gathered and analyzed through structural equation modeling (SEM) operated by AMOS 24. Organizational support, along with customer awareness, was found to have strong effects on adoption behavior because perceived security functions as the primary determining factor. The research results indicated that regulatory compliance failed to have a direct effect on adoption behavior. The study validated construct reliability and validity through confirmatory factor analysis (CFA) since all Cronbach’s alpha values surpassed 0.80 and the composite reliability and average variance extracted measurements fell within acceptable ranges. The study model demonstrated an acceptable fit, as indicated by RMSEA (0.045), CFI (0.942), TLI (0.930), and χ²/df (2.18). Digital transformation in insurance requires organizational programs that provide team-based customer education while maintaining robust privacy measures.

As health systems worldwide increasingly focus on mitigating the burden of non-communicable diseases, the strategic role of insurance schemes in facilitating early detection and preventive care, thereby reducing the substantial costs associated with advanced-stage treatment, has become a critical area of policy and research attention. This study aims to evaluate the impact of various health financing models, specifically voluntary, compulsory, and social insurance, on the burden of cardiovascular diseases and neoplasms, measured by Disability-Adjusted Life Years (DALYs), across working-age and older populations. The analysis is based on unbalanced panel data from 51 countries covering the period 2000–2021, drawing from the Global Burden of Disease database for DALY rates and the OECD and WHO Global Health Expenditure Database for health financing indicators. Fixed and random effects panel regression models with clustered robust standard errors were employed to estimate the associations. Results show that voluntary private insurance significantly reduces DALY rates from cardiovascular diseases, by approximately 19-28%, among working-age (15-49) and older adults (50-69). Compulsory and social insurance models also exhibit protective effects, but of smaller magnitude. Government health financing schemes similarly correlate with improved outcomes. In contrast, enterprise-based financing is positively associated with higher DALY rates, especially in older age groups. Insurance schemes demonstrate weaker and more inconsistent associations for neoplasms, with compulsory insurance and government schemes showing the most stable links to reduced burden among older adults.

AI technology integration into the Indian insurance industry promises many benefits, but its acceptance among older adults remains a challenge. Previous studies have paid insufficient attention to older adults’ unique needs and concerns in the context of AI-driven insurance service acceptance in India. The purpose of the study is to evaluate the acceptance of AI-powered digital insurance services among older adults in Kerala, India, from the perspective of customer satisfaction. This exploratory study employed the insights of the Technology Acceptance Model (TAM), the Information System Continuance Model (ISCM), and the Customer Satisfaction (C-SAT) method. Data were collected by conducting interviews in September 2024 with 20 older adults using AI-powered insurance services. Findings indicate a positive trend in adopting digital insurance services among older adults. However, the mean C-SAT scores for Perceived Usefulness and Perceived Ease of Use were 58% and 56%, respectively. Customer satisfaction scores for chatbot services and automated claims processing stood at 55% and 50%, respectively. These calculated scores are below the American Customer Satisfaction Index (ACSI) benchmark of 77.9% for Q2 2024. The participants of the study also expressed concerns regarding the use of AI-powered digital insurance services, citing inadequate user training facilities, fears of financial loss, privacy issues, and security and safety concerns. These results suggest the need for enhancements in AI interface design, user training, and customer support to better meet the unique needs and concerns of older adults and improve overall satisfaction.