As insurers increasingly behave like financial intermediaries and actively participate in capital markets, understanding the dependence structure between insurance and financial risks becomes crucial for insurers' operations. This paper studies dynamic equilibrium insurance pricing when insurers face ambiguity about the correlation between insurance and financial risks and optimally choose underwriting and investment strategies under worst-case beliefs. Correlation ambiguity can generate multiple equilibrium regimes. Contrary to conventional intuition, we find ambiguity does not necessarily increase insurance prices nor reduce insurers' utility.
Amanda Le, Liisa S. Smith, Stephanie B. Wheeler
et al.
Background: Primary testing for high-risk human papillomavirus (HPV) via self-collection can increase cervical cancer screening rates. In the United States, federally qualified health center (FQHC) patients often have low incomes, lack health insurance, are medically underserved, and are screened less than the national average. Implementation of HPV self-collection can increase cervical cancer screenings among FQHCs. Objectives: To assess the potential impact of implementing HPV self-collection with FQHC patients by considering perspectives of frontline clinical and administrative staff and leadership to identify patient-focused implementation considerations. Design: This qualitative study utilized focus groups and key informant interviews, and transcripts were analyzed using a coding-based thematic analysis. Emergent themes regarding self-collection implementation perspectives were mapped onto Consolidated Framework for Implementation Research constructs to identify potential facilitators and barriers to implementation for FQHC patient populations. Methods: Participants from six FQHCs in North Carolina were identified. Forty-five clinical and administrative staff participated in focus groups. One chief executive officer, senior level administrator, chief medical officer, and clinical data manager from each FQHC ( N = 24) were interviewed one-on-one. Coding-based thematic analysis was applied to focus group and interview transcripts to uncover emerging themes. Results: Interviewees indicated that HPV self-collection can be advantageous to patients who do not routinely visit the clinic due to socioeconomic and cultural barriers. Programs must consider these barriers and patient literacy to ensure proper self-collection utilization. For example, FQHC patients may benefit from illustrated instructions for proper self-collection procedures. Conclusion: Tailoring an HPV self-collection implementation to FQHC patient populations may be an important strategy for increasing screening.
Background Diabetes is a leading global public health challenge with 537 million cases worldwide in 2021 and significant economic and societal burdens. Despite global initiatives, feasibility constraints limit widespread implementation of diabetes prevention/control measures. The key to facilitating the implementation of diabetes prevention and control measures lies in prioritise them. Objectives This study aimed to determine the priority of diabetes prevention and control measures in China from the feasibility perspective. Methods From January to February 2021, an online survey was administered to professionals specifically responsible for implementing chronic disease prevention and control programs in 488 national demonstration areas in China. The survey assessed the importance and feasibility of 44 diabetes prevention and control measures across 10 dimensions. The Importance-Performance Analysis (IPA) model categorized these indicators into four quadrants: Highest Priority (high importance and feasibility), Priority Improvement (high importance, low feasibility), Lowest Priority (low importance and feasibility), and Secondary Improvement (low importance, high feasibility). Results Quadrant 1 (‘Highest Priority’) included health education, community action, high-risk discovery and intervention, and medical insurance and family doctors, which should be maintained and strengthened. Quadrant 2 (‘Priority Improvement’) included patient management and complication screening, necessitating targeted efforts to enhance implementation. Quadrant 3 (‘Lowest Priority’) included personal health services assessment follow-up, environmental support, sugar reduction policy, and diabetes co-infection prevention, indicating a risk of overinvestment. No indicators were categorized into Quadrant 4 (‘Secondary Priority’). Conclusions Prioritizing diabetes prevention and control measures based on feasibility maximizes the utilization of limited resources, providing implementable recommendations for policymakers.
Shibiru Ayalew Melesse, Oliver Scholz, Abdi Yuya Ahmad
Digital innovation is critical for financial institutions seeking to remain competitive in today's fast-evolving technological landscape. However, firms in developing countries, like Ethiopia, face significant challenges in preparing for such innovations. This study assessed the readiness of financial institutions in Ethiopia for digital innovation and examined the impact it has on their effectiveness in implementing innovation. Primary data was collected from banks, insurance companies, and microfinance institutions, and analyzed using multiple regression and t-tests of equality between two means. The findings revealed that the overall readiness level of Ethiopian financial institutions is unsatisfactory, regardless of the type of financial institution. Additionally, managers generally perceive their organizations as more prepared for digital transformation than their employees do. These results highlight the need for Ethiopian financial institutions to address internal perception gaps and to improve readiness in underperforming areas to foster a unified approach to innovation. Moreover, policymakers are suggested to prioritize strengthening digital infrastructure, enacting supportive regulatory frameworks, and promoting financial sector digitalization through incentives.
We provide a survey of how techniques developed for the modelling of extremes naturally matter in insurance, and how they need to and can be adapted for the insurance applications. Topics covered include truncation, tempering, censoring and regression techniques. The discussed techniques are illustrated on concrete data sets.
With the rise of emerging risks, model uncertainty poses a fundamental challenge in the insurance industry, making robust pricing a first-order question. This paper investigates how insurers' robustness preferences shape competitive equilibrium in a dynamic insurance market. Insurers optimize their underwriting and liquidity management strategies to maximize shareholder value, leading to equilibrium outcomes that can be analytically derived and numerically solved. Compared to a benchmark without model uncertainty, robust insurance pricing results in significantly higher premiums and equity valuations. Notably, our model yields three novel insights: (1) The minimum, maximum, and admissible range of aggregate capacity all expand, indicating that insurers' liquidity management becomes more conservative. (2) The expected length of the underwriting cycle increases substantially, far exceeding the range commonly reported in earlier empirical studies. (3) While the capacity process remains ergodic in the long run, the stationary density becomes more concentrated in low-capacity states, implying that liquidity-constrained insurers require longer to recover. Together, these findings provide a potential explanation for recent skepticism regarding the empirical evidence of underwriting cycles, suggesting that such cycles may indeed exist but are considerably longer than previously assumed.
This paper outlines the specific provisions of Italian legislation regarding workers’ exposure to electromagnetic fields (EMFs) from 0 Hz to 300 GHz compared to the minimum health and safety requirements set in European Directive 2013/35/EU. In particular, the path to be followed to assess and manage occupational exposure to EMFs is outlined in relation to the distinction between ‘professional’ and ‘non-professional’ exposure of workers, as well as to the precautionary limits regarding exposures from power lines (50 Hz) and broadcast and telecommunication fixed systems (100 kHz–300 GHz) established by Italian regulations. The reasons underlying such an approach—mainly relying on the intent to reconcile scientific evidence with risk perception in public opinion—are analysed and discussed with the aim of increasing the knowledge of national regulatory provisions on occupational risk assessment, which may be more stringent than the requirements envisaged by international guidelines and community regulations.
Industrial safety. Industrial accident prevention, Medicine (General)
Grace Gomez Felix Gomez, Mei Wang, Zasim A. Siddiqui
et al.
Abstract Background Decreased salivary secretion is not only a risk factor for carious lesions in Sjögren’s disease (SD) but also an indicator of deterioration of teeth with every restorative replacement. This study determined the longevity of direct dental restorations placed in patients with SD using matched electronic dental record (EDR) and electronic health record (EHR) data. Methods We conducted a retrospective cohort study using EDR and EHR data of Indiana University School of Dentistry patients who have a SD diagnosis in their EHR. Treatment history of patients during 15 years with SD (cases) and their matched controls with at least one direct dental restoration were retrieved from the EDR. Descriptive statistics summarized the study population characteristics. Cox regression models with random effects analyzed differences between cases and controls for time to direct restoration failure. Further the model explored the effect of covariates such as age, sex, race, dental insurance, medical insurance, medical diagnosis, medication use, preventive dental visits per year, and the number of tooth surfaces on time to restoration failure. Results At least one completed direct restoration was present for 102 cases and 42 controls resulting in a cohort of 144 patients’ EDR and EHR data. The cases were distributed as 21 positives, 57 negatives, and 24 uncertain cases based on clinical findings. The average age was 56, about 93% were females, 54% were White, 74% had no dental insurance, 61% had public medical insurance, < 1 preventive dental visit per year, 94% used medications and 93% had a medical diagnosis that potentially causes dry mouth within the overall study cohort. About 529 direct dental restorations were present in cases with SD and 140 restorations in corresponding controls. Hazard ratios of 2.99 (1.48–6.03; p = 0.002) and 3.30 (1.49–7.31, p-value: 0.003) showed significantly decreased time to restoration failure among cases and positive for SD cases compared to controls, respectively. Except for the number of tooth surfaces, no other covariates had a significant influence on the survival time. Conclusion Considering the rapid failure of dental restorations, appropriate post-treatment assessment, management, and evaluation should be implemented while planning restorative dental procedures among cases with SD. Since survival time is decreased with an increase in the number of surfaces, guidelines for restorative procedures should be formulated specifically for patients with SD.
This paper analyzes the equilibrium of insurance market in a dynamic setting, focusing on the interaction between insurers' underwriting and investment strategies. Three possible equilibrium outcomes are identified: a positive insurance market, a zero insurance market, and market failure. Our findings reveal why insurers may rationally accept underwriting losses by setting a negative safety loading while relying on investment profits, particularly when there is a negative correlation between insurance gains and financial returns. Additionally, we explore the impact of regulatory frictions, showing that while imposing a cost on investment can enhance social welfare under certain conditions, it may not always be necessary.
Machine Learning (ML) has gained popularity in actuarial research and insurance industrial applications. However, the performance of most ML tasks heavily depends on data preprocessing, model selection, and hyperparameter optimization, which are considered to be intensive in terms of domain knowledge, experience, and manual labor. Automated Machine Learning (AutoML) aims to automatically complete the full life-cycle of ML tasks and provides state-of-the-art ML models without human intervention or supervision. This paper introduces an AutoML workflow that allows users without domain knowledge or prior experience to achieve robust and effortless ML deployment by writing only a few lines of code. This proposed AutoML is specifically tailored for the insurance application, with features like the balancing step in data preprocessing, ensemble pipelines, and customized loss functions. These features are designed to address the unique challenges of the insurance domain, including the imbalanced nature of common insurance datasets. The full code and documentation are available on the GitHub repository. (https://github.com/PanyiDong/InsurAutoML)
We mathematically demonstrate how and what it means for two collective pension funds to mutually insure one another against systematic longevity risk. The key equation that facilitates the exchange of insurance is a market clearing condition. This enables an insurance market to be established even if the two funds face the same mortality risk, so long as they have different risk preferences. Provided the preferences of the two funds are not too dissimilar, insurance provides little benefit, implying the base scheme is effectively optimal. When preferences vary significantly, insurance can be beneficial.
The gains from insurance arise from the transfer of income across states. Yet, by requiring that the premium be paid up front, standard insurance products also transfer income across time. We show that this intertemporal transfer can help explain low insurance demand, especially among the poor, and in a randomized control trial in Kenya we test a crop insurance product which removes it. The product is interlinked with a contract farming scheme: as with other inputs, the buyer of the crop offers the insurance and deducts the premium from farmer revenues at harvest time. The take-up rate for pay-at-harvest insurance is 72 percent, compared to 5 percent for the standard pay-up-front contract, and the difference is largest among poorer farmers. Additional experiments and outcomes provide evidence on the role of liquidity constraints, present bias, and counterparty risk, and find that enabling farmers to commit to pay the premium just 1 month later increases demand by 21 percentage points. (JEL G22, I32, O13, O16, Q12, Q14)