Search Results (103)

Coinfections with hepatitis C virus (HCV) or human immunodeficiency virus (HIV) among individuals with chronic hepatitis B (CHB) are associated with worse clinical outcomes but remain understudied due to their low prevalence and the sensitivity of associated data. This nationwide, cross-sectional study utilized claims data from the Korean Health Insurance Review and Assessment Service (2014–2021) to investigate the prevalence, comorbidities, treatment patterns, and liver-related complications among patients with HBV monoinfection, HBV/HIV, HBV/HCV, or triple coinfection. Among over 4.5 million patients with chronic hepatitis B, the prevalence of HIV and HCV coinfection ranged from 0.05 to 0.07% and 0.77 to 1.00%, respectively. Patients with HBV/HCV coinfection were older and had significantly higher rates of hypertension, diabetes, dyslipidemia, and major adverse liver outcomes, including hepatocellular carcinoma and liver transplantation, compared to other groups. HBV/HIV coinfection was more common in younger males and was associated with higher dyslipidemia. The use of HBV antivirals increased over time across all groups. These findings highlight the distinct clinical characteristics and unmet needs of coinfected populations, underscoring the importance of tailored screening and management strategies in HBV-endemic settings. Full article

This study evaluates the current Spanish insurance framework for catastrophic flood risk, administered by the Consorcio de Compensación de Seguros (CCS), based on nationwide loss data reported by the CCS for the period 1996–2020. The analysis of historical claims data enables a clear differentiation between frequent, low-cost events and infrequent, high-impact catastrophes. While the CCS has fulfilled a critical role in post-disaster compensation, the findings highlight the parallel need for ex ante risk mitigation strategies. The study proposes a more efficient, two-tier risk coverage model. Events whose impacts can be managed through standard insurance mechanisms should be underwritten by private insurers using actuarially fair premiums. In contrast, events with catastrophic implications—due to their scale or financial impact—should be addressed through general solidarity mechanisms, centrally managed by the CCS. Such a risk segmentation would improve the financial sustainability of the system and create fiscal space for prevention-oriented incentives. The current design of the CCS scheme may generate moral hazard, as flood exposure is not explicitly priced into the premium structure. Empirical findings support a shift towards a more transparent, incentive-aligned model that combines collective risk sharing with individual risk responsibility—an essential balance for effective climate adaptation and long-term resilience. Full article

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy. Full article

The coronavirus (COVID-19) pandemic has profoundly affected public health and socio-economic structures globally. This research conducted a post-COVID-19 analysis of the role of fiscal support interventions on COVID-19 health regulations such as mandatory non-pharmaceutical interventions like face masks, social distancing, periodic lockdowns which include restrictions on movement, and socio-economic dimensions. This quantitative research obtained 302 responses from different households in the Eastern Cape, Gauteng, Kwa-Zulu Natal, and Limpopo Provinces in South Africa. The results reveal that the relief fund (R350 unemployment grant, unemployment insurance fund claim, and food parcel distribution, among others) mediated the relationship between COVID-19 health regulations and poverty levels and the relationship between COVID-19 health regulations and health and well-being. The relief fund also mediated the relationship between COVID-19 health regulations and employment levels. Support packages from the R500 billion government support, which included loan guarantees, job support, tax and payment deferrals and holidays, social grants, wage guarantees, health interventions, and municipalities support, moderate the relationship between COVID-19 health regulations and the family and social support. These results validate the impact of the fiscal support intervention by the government in mitigating its emergency intervention with COVID-19 health regulations. This strengthens the theory of intervention, highlighting that multiple dynamics make interventions complex as shown by mediation and moderation results. Furthermore, this study highlights intervention being central to the management of the crisis. The study highlights the importance of comprehensive intervention for future preparedness, thus advancing the crisis–intervention perspective. Advances in these areas are critical to mitigate the impact of the next pandemic or similar major events in society. This can be achieved through improved pandemic timely response with effective economic stimulus, social relief, strong legal framework, and anti-corruption policies. Full article

The estimation of age-to-age development factors is fundamental to loss reserving, with direct implications for risk management and regulatory compliance in the auto insurance sector. The precise and robust estimation of these factors underpins the credibility of case reserves and the effective management of future claim liabilities. This study investigates the underlying structure and sources of variability in development factor estimates by applying multivariate statistical techniques to the analysis of development triangles. Departing from conventional univariate summaries (e.g., mean or median), we introduce a comprehensive framework that incorporates temporal clustering of development factors and addresses associated modeling complexities, including high dimensionality and temporal dependency. The proposed methodology enhances interpretability and captures latent structures in the data, thereby improving the reliability of reserve estimates. Our findings contribute to the advancement of reserving practices by offering a more nuanced understanding of development factor behavior under uncertainty. Full article

Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted global healthcare. This study explores the effects of perioperative COVID-19 infection on postoperative outcomes, aiming to refine risk assessment and enhance personalized perioperative care using a comprehensive dataset from the Korean National Health Insurance Service. This analysis extends previous research by providing a large-scale validation of risk factors associated with COVID-19 in a perioperative setting. Methods: In this retrospective cohort study, we analyzed data from 2,903,858 patients who underwent surgery under general anesthesia between January 2020 and December 2021. Patients were categorized into COVID-19 (+) and COVID-19 (−) groups within 30 d before or after surgery. Logistic regression models were used to identify independent risk factors for mortality and pulmonary complications. Results: After propensity score matching, the final cohort comprised 19,235 patients (COVID-19 (+): 3847; COVID-19 (−): 15,388). The COVID-19 (+) group had significantly higher overall mortality than the COVID-19 (−) group. No significant difference was observed between the groups concerning 30 d mortality. Pulmonary complications, including pneumonia and acute respiratory distress syndrome, were significantly more frequent in the COVID-19 (+) group. The independent predictors of 30 d mortality included advanced age, emergency surgery, and the American Society of Anesthesiologists physical status classification. Conclusions: Our study confirms that perioperative COVID-19 infection significantly elevates overall mortality and pulmonary complications, emphasizing the necessity of tailored perioperative management. Incorporating individual risk factors into care protocols not only reduces risks for surgical patients but also enhances treatment approaches. These findings advocate for the implementation of personalized medicine principles in surgical settings to improve patient outcomes during and after the COVID-19 pandemic. This research uses a comprehensive national medical claims dataset to set new standards for studying pandemic health impacts and improving clinical strategies. Full article

Claim management is a critical process for insurance companies, requiring fairness, transparency, and efficiency to maintain policyholder trust and minimize financial impact. In our previous work, we introduced Insoore AI, an insurtech solution leveraging deep learning-based computer vision to automate car damage recognition and localization from user-provided pictures. While this approach demonstrated the potential of AI in claims management, it faced limitations in terms of performance and computational efficiency due to resource constraints. In this study, we present an improved version of Insoore AI, enabled by the High-Performance Computing (HPC) resources offered by the Booster module of LEONARDO HPC system located at the CINECA datacenter in Bologna, Italy. By leveraging the advanced computational capabilities of the above-mentioned HPC infrastructure, we trained larger and more complex deep learning models, processed higher-resolution images, and significantly reduced training and inference times. Our results show marked performance improvements in terms of damage detection, paving the way for more efficient, more effective and scalable claims management solutions. This work underscores the transformative potential of HPC resources in advancing AI-driven innovations in the insurance sector and is to be regarded as an improvement on the contribution of our previous work, enabled by relying on the DiffusionDet architecture and on a Swin Transformer backbone to solve the problem of automatic car damage detection and classification. Full article

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for addressing extreme catastrophic events that result in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance on the level of risk. Our starting point is to use a hierarchical risk aggregation method which was initially based on two-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over- or underestimation of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines. Full article

This study investigates the impacts of autonomous vehicles (AVs) on the insurance industry from the viewpoint of insurance companies, highlighting the necessity for adaptation due to technological advancements. The research is motivated by the gap in understanding between traditional insurers and automaker-backed insurance services regarding AV implications. The purpose is to identify potential impacts, evaluate the level of concern among diverse insurance companies, and examine their differing perspectives. The methodology includes a literature review, the Analytic Hierarchy Process (AHP), and Spearman correlation analysis. The literature review clarifies the definition of AVs and their impacts on traditional insurance. The AHP assesses the level of concern among insurance companies, and Spearman correlation analysis explores the similarities and differences in perspectives. The findings show that insurance companies largely agree on the transformative impacts of AVs. The primary effects are in “Updates in Insurance Business Operations” and the “Emergence of New Risks”, with less impact on “Changes in the Insurance Market”. A major concern is the complexity of multi-party liability claims. Companies differ in their focus on specific impacts like legal frameworks or system malfunctions, but share concerns about multi-party liability, system malfunctions, and legal gaps. The study anticipates minor impacts on market dynamics and traditional insurance models. The conclusions emphasize that AVs will significantly impact the insurance industry, requiring innovation and adaptation to maintain competitiveness. This includes developing new products, optimizing processes, and collaborating with stakeholders. The study has several implications: customized insurance products, optimized no-fault claims processes, collaborations with automakers and tech firms, data-driven risk assessments, enhanced risk management, and adapting traditional models. Recommendations include building loss experience databases, adopting no-fault insurance, strategic partnerships, developing customized products, strengthening risk management and cybersecurity, monitoring regulations, adjusting traditional models, focusing on product liability insurance, and training professionals. Full article

Background: Rejected medical claims pose a significant challenge for healthcare facilities accredited by Tanzania’s National Health Insurance Fund (NHIF). Despite the NHIF’s role in reducing out-of-pocket costs, claim rejections have been a persistent issue, largely due to documentation errors, coding mistakes, and non-compliance with NHIF regulations. This study determined the patterns of rejected claims and the strategies employed by NHIF-accredited hospitals to mitigate these challenges. Methodology: This cross-sectional study was conducted between July and August 2024 and used quantitative and qualitative approaches. The study utilized secondary data (August 2023 to January 2024) on the rejected claims from 46 healthcare facilities (HFs) and key informant interviews from the respective selected facilities. Descriptive data analysis was carried out using STATA version 15 and qualitative data analysis was conducted using NViVo2 version 12 software. Results: A total of 46 public (27) and private (19) HFs were included in this study. The data revealed significant variation in the average number of items rejected per claim across HFs, ranging from 0.21 in a regional referral hospital to 1.21 in a zonal hospital. Non-adherence to standard treatment guidelines (STGs) was significantly more common (p < 0.001) in polyclinics, accounting for 17.2% of the items rejected, and with the lowest number (0.8%) seen in zonal hospitals. Overutilization (drugs and investigations) was commonly reported in all HFs, ranging from 12.5% in polyclinics to 31.8% in district hospitals (p < 0.001). Non-applicable consultation charges were only reported in one zonal hospital. To mitigate these rejections, HFs implemented strategies such as immediate error verification, regular communication with NHIF, staff training, technology use, and regular supervision by the internal audit units. Despite these efforts, challenges persisted, particularly those stemming from complex NHIF policies, which account for most rejections in zonal health facilities. Conclusions: There are significant variations in rejection patterns among HFs, with attendance date anomalies, non-adherence to STGs, NHIF pricing, and overutilization being the most common reasons across all HFs. Strategies to address rejections should be tailored to specific health facilities, coupled with electronic systems that will detect errors during patient management. Full article

Hospital and health system bankruptcies and closures continue to rise in the United States. They are troubling news not only for patients and communities but also for insurance companies. Hospital closures often lead to higher costs for insurers due to increased claim denials, delayed payments, reduced provider network and access to care, higher out-of-network costs, and a disruption of our healthcare system. These factors ultimately impact the health insurance companies’ bottom lines as well as their ability to manage patient care effectively with the risk of causing customer/patient dissatisfaction. Insurance companies can help prevent hospital closures, especially in rural areas, by implementing some of the following mechanisms: timely and adequate payments; improved patient-centric payment systems; and standby capacity payments to cover minimum fixed costs. Such early strategic investments have the potential to offset the higher costs for insurance companies associated with hospital closures and improve the sustainability of the U.S. healthcare system. Full article

Efficient cattle healthcare management is vital for ensuring productivity and welfare in dairy production, yet traditional record-keeping methods often lack transparency, security, and efficiency, leading to challenges in livestock product quality and healthcare. This study introduces a novel framework leveraging Zero Knowledge (ZK)-Rollups-enhanced Layer 2 blockchain and Non-Fungible Tokens (NFTs) to address these issues. NFTs serve as secure digital certificates for individual cattle health records, ensuring transparency and traceability. ZK-Rollups on the Layer 2 blockchain enhance scalability, privacy, and cost-efficiency, while smart contracts automate key processes such as veterinary scheduling, medication delivery, and insurance claims, minimizing administrative overhead. Performance evaluations reveal significant advancements, with transaction delays of 4.1 ms, throughput of 249.8 TPS, gas costs reduced to 26,499.76 Gwei, and a time-to-finality of 1.1 ms, achieved through ZK-SNARKs (ZK-Succinct Non-Interactive Arguments of Knowledge) integration. These results demonstrate the system’s potential to revolutionize cattle healthcare management by combining transparency, security, and operational efficiency. Full article

In this paper, we propose a weighted Lindley (NWLi) model for the analysis of extreme historical insurance claims. It extends the classical Lindley distribution by incorporating a weight parameter, enabling more flexibility in modeling insurance claim severity. We provide a comprehensive theoretical overview of the new model and explore two practical applications. First, we investigate the mean-of-order P (MOOP_(P)) approach for quantifying the expected claim severity based on the NWLi model. Second, we implement a peaks over a random threshold (PORT) analysis using the value-at-risk metric to assess extreme claim occurrences under the new model. Further, we provide a simulation study to evaluate the accuracy of the estimators under various methods. The proposed model and its applications provide a versatile tool for actuaries and risk analysts to analyze and predict extreme insurance claim severity, offering insights into risk management and decision-making within the insurance industry. Full article

Objective: This study investigated the impact of recent syphilis infection on postoperative outcomes following total knee arthroplasty (TKA). We hypothesized that patients with a documented history of syphilis infection would experience a higher rate of postoperative complications compared to those without such a history. Methods: We conducted a retrospective cohort analysis using a national insurance claims database. Our study population included 237,360 patients who underwent primary TKA between 2005 and 2024. Patients were classified into two groups based on the presence (+Syph) or absence (−Syph) of a syphilis diagnosis within one year prior to the TKA. We evaluated the rates of several postoperative complications at 30 days postsurgery, including infection, hematologic issues, and cardiac events. Statistical analyses between groups was performed using chi-squared tests and Fisher’s exact tests. Routine demographic data such as age, sex, race, and comorbidities were also analyzed. Results: Among the 237,360 TKA patients, we identified 71 with a history of syphilis within one year of their surgery. The +Syph group exhibited significantly higher rates of periprosthetic infection (4.23% vs. 0.81%, p = 0.001), need for manipulation under anesthesia (MUA) at four months (7.04% vs. 2.82%, p = 0.032), deep venous thrombosis (4.23% vs. 1.27%, p = 0.026), periprosthetic fracture (2.82% vs. 0.23%, p < 0.001), and pneumonia (2.82% vs. 0.62%, p = 0.019) within 30 days postTKA. No significant differences were observed in 30-day mortality, deep or superficial surgical site infections, wound dehiscence, blood loss anemia, or transfusion requirements. Additionally, rates of acute renal failure, pulmonary embolism, and cardiac events did not differ significantly between groups. Demographically, patients in the syphilis cohort had a higher prevalence of smoking and diabetes preoperatively within one year of their surgical date. Conclusions: A documented syphilis diagnosis within one year of TKA significantly affects postoperative outcomes, increasing the rates of prosthetic joint infection, MUA, deep venous thrombosis, periprosthetic fracture, and pneumonia. These findings underscore the need for heightened vigilance in the pre- and postoperative management of patients with a history of syphilis infection undergoing TKA. Further research is warranted to explore the relationship between prior syphilis infection and TKA outcomes, as well as to develop strategies to mitigate this increased risk. Full article

Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, and, if necessary, dispute resolution. Fair, transparent and timely claims management is crucial for maintaining policyholders’ trust while also limiting the financial impact on the insurer. Technological innovations, such as the use of artificial intelligence and automation, are positively influencing this sector, enabling faster and more effective claims management. This study reports on Insoore AI, an insurtech solution that aims to automate a portion of claims management by integrating a computer vision solution based on some latest developments in deep learning to automatically recognize and localize car damage from user-provided pictures. Full article