Hasil untuk "Insurance"

J. Gruber

Sadiq Y Patel, A. Mehrotra, H. Huskamp et al.

This cohort study examines trends in the use of telemedicine and in-person outpatient visits in 2020 among a national sample of 16.7 million individuals with commercial or Medicare Advantage insurance.

K. Kugeler, Amy M Schwartz, M. Delorey et al.

By using commercial insurance claims data, we estimated that Lyme disease was diagnosed and treated in ≈476,000 patients in the United States annually during 2010–2018. Our results underscore the need for accurate diagnosis and improved prevention.

Paul J. Gertler, J. Gruber

Adam B. Smith, Richard W. Katz

J. Conesa, S. Kitao, Dirk Krueger

Frank Cremer, Barry Sheehan, M. Fortmann et al.

Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

Marina Bykova, Ehud Karavani, Michael Danziger et al.

Alzheimer's disease (AD), a leading global cause of dementia, disability, and mortality, represents a critical unmet need for effective therapeutic interventions. Drug repurposing offers an expedited pathway to address this challenge compared to traditional drug development. Here, we leveraged network-based prediction and real-world patient data validation, a comprehensive strategy to identify unanticipated therapeutic applications for existing medications. Traumatic brain injury (TBI), a major risk factor for earlier and more severe AD, exhibits heterogenous clinical outcomes influenced by genetic susceptibility, suggesting that TBI-diagnosed populations represent a cohort enriched for neurodegeneration vulnerability. Building on this premise, we integrated network-based multi-omics and endophenotypic disease modules from individuals with TBI and AD histories with large real-world patient data analysis from insurance claims to prioritize repurposable drugs. A network proximity algorithm applied to TBI- and AD-associated gene sets identified statistically ranked candidates, including doxycycline and irbesartan. We then assessed all candidates' AD risk reduction potential using a nationwide Medicare database encompassing nearly 9 million individuals. In a retrospective observational study of AD-free elderly individuals monitored for up to 3 years, propensity-score adjusted survival analyses demonstrated a significantly reduced cumulative incidence of AD in doxycycline and irbesartan-prescribed individuals, with risk ratios of 0.92 and 0.83, respectively, at a 95 % confidence interval. These findings nominate doxycycline and irbesartan as potential repurposable drugs for AD and demonstrate the translational potential of synergizing network-based prediction with real-world patient evidence in drug repurposing for neurodegenerative disease if broadly applied.

Mei Yang, Tiankai Wang

IntroductionUnequal healthcare access is linked to disparities in health outcomes. Public transit plays a critical role in promoting equitable healthcare access, particularly for disadvantaged populations. This study aims to assess disparities in hospital access via public transit in Austin, Texas, while considering socioeconomic and demographic factors.MethodsWe analyzed 30 hospitals using data from Definitive Healthcare, alongside demographic and socioeconomic factors for 283 census tracts in and around Austin, Texas, obtained from the U. S. Census Bureau. Variables included the percentage of the population who are Black or African American, Hispanic or Latino, uninsured, or have incomes below the poverty level. Using the TravelTime Isochrone API, we delineated one-hour public transit catchment areas for each hospital and overlaid them with demographic and socioeconomic data to examine spatial disparities in healthcare access and identify underserved communities.ResultsOverall, people in the western and eastern parts of the city lack hospital service coverage accessible by public transit within 1 hour. Of the 283 census tracts, 160 are either partially covered (125 tracts) or not covered at all (35 tracts), with 72 of the partially covered tracts having less than 50 percent area coverage. The eastern area has higher proportions of Black or African American, Hispanic or Latino, and uninsured populations, reflecting greater disparities.DiscussionThe results revealed notable disparities in healthcare access via public transit, where limited hospital coverage overlaps with high social and economic vulnerability. Targeted transit and healthcare planning for underserved areas and populations is needed to reduce these inequities.

Dariusz Krawczyk, Alina Yefimenko, Iryna Pozovna et al.

A well-capitalised bank system is a key element for securing macroeconomic stability. By applying a comprehensive approach to managing the capitalisation of banks, policymakers, regulators, and financial institutions can strengthen the resistance of the financial system, reduce system risks, and contribute to macroeconomic stability. The goal of the research is to develop an international functional benchmarking model for managing bank capital in the context of securing macroeconomic stability for 34 European countries with different population income levels from 2010 to 2022 based on World Bank data. The aim is achieved through the implementation of the defined stages of benchmarking modelling. The international functional benchmarking model for bank capital management in the context of macroeconomic stability has been developed by defining the qualitative and quantitative characteristics of the leading countries, chosen based on the corresponding ranking. The three groups of benchmarks are identified: institutional and innovative approaches (based on Swiss and Luxembourg practices), monetary and credit approaches (based on Sweden and Iceland’s practices), and preventive and regulatory approaches (based on Norway and Finland’s practices). The research results can be used in the processes of a bank’s risk management and formation and regulation of capital adequacy by bank management, as well as when developing state socioeconomic and financial policies.

Arthur Charpentier, Philipp Ratz

Over the past decade alternatives to traditional insurance and banking have grown in popularity. The desire to encourage local participation has lead products such as peer-to-peer insurance, reciprocal contracts, and decentralized finance platforms to increasingly rely on network structures to redistribute risk among participants. In this paper, we develop a comprehensive framework for linear risk sharing (LRS), where random losses are reallocated through nonnegative linear operators which can accommodate a wide range of networks. Building on the theory of stochastic and doubly stochastic matrices, we establish conditions under which constraints such as budget balance, fairness, and diversification are guaranteed. The convex order framework allows us to compare different allocations rigorously, highlighting variance reduction and majorization as natural consequences of doubly stochastic mixing. We then extend the analysis to network-based sharing, showing how their topology shapes risk outcomes in complete, star, ring, random, and scale-free graphs. A second layer of randomness, where the sharing matrix itself is random, is introduced via Erdős--Rényi and preferential-attachment networks, connecting risk-sharing properties to degree distributions. Finally, we study convex combinations of identity and network-induced operators, capturing the trade-off between self-retention and diversification. Our results provide design principles for fair and efficient peer-to-peer insurance and network-based risk pooling, combining mathematical soundness with economic interpretability.

Hansjörg Albrecher, Filip Lindskog, Hervé Zumbach

Cost-of-capital valuation is a well-established approach to the valuation of liabilities and is one of the cornerstones of current regulatory frameworks for the insurance industry. Standard cost-of-capital considerations typically rely on the assumption that the required buffer capital is held in risk-less one-year bonds. The aim of this work is to analyze the effects of allowing investments of the buffer capital in risky assets, e.g.~in a combination of stocks and bonds. In particular, we make precise how the decomposition of the buffer capital into contributions from policyholders and investors varies as the degree of riskiness of the investment increases, and highlight the role of limited liability in the case of heavy-tailed insurance risks. We present a combination of general theoretical results, explicit results for certain stochastic models and numerical results that emphasize the key findings.

Din Amir, Bar Hoter, Moran Koren

This study examines strategic behavior in crowdfunding using a large-scale online experiment. Building on the model of Arieli et. al 2023, we test predictions about risk aversion (i.e., opting out despite seeing a positive private signal) and mutual insurance (i.e., opting in despite seeing a negative private signal) in a static, single-shot crowdfunding game, focusing on informational incentives rather than dynamic effects. Our results validate key theoretical predictions: crowdfunding mechanisms induce distinct strategic behaviors compared to voting, where participants are more likely to follow private signals (odds ratio: 0.139, $p < 0.001$). Additionally, the study demonstrates that higher signal accuracy (85\% vs. 55\%) decreases risk aversion (odds ratio: 0.414, $p = 0.024$) but increases reliance on mutual insurance (odds ratio: 2.532, $p = 0.026$). However, contrary to theory, increasing the required participation threshold (50\% to 80\%) amplifies risk aversion (odds ratio: 3.251, $p = 0.005$), which, pending further investigation, may indicate cognitive constraints. Furthermore, we show that while mutual insurance supports participation, it may hinder information aggregation, particularly as signal accuracy increases. These findings advance crowdfunding theory by confirming the impact of informational incentives and identifying behavioral deviations that challenge standard models, offering insights for platform design and mechanism refinement.

Fei Huang, Silvana M. Pesenti

This paper introduces marginal fairness, a new individual fairness notion for equitable decision-making in the presence of protected attributes such as gender, race, and religion. This criterion ensures that decisions based on generalized distortion risk measures are insensitive to distributional perturbations in protected attributes, regardless of whether these attributes are continuous, discrete, categorical, univariate, or multivariate. To operationalize this notion and reflect real-world regulatory environments (such as the EU gender-neutral pricing regulation), we model business decision-making in highly regulated industries (such as insurance and finance) as a two-step process: (i) a predictive modeling stage, in which a prediction function for the target variable (e.g., insurance losses) is estimated based on both protected and non-protected covariates; and (ii) a decision-making stage, in which a generalized distortion risk measure is applied to the target variable, conditional only on non-protected covariates, to determine the decision. In this second step, we modify the risk measure such that the decision becomes insensitive to the protected attribute, thus enforcing fairness to ensure equitable outcomes under risk-sensitive, regulatory constraints. Furthermore, by utilizing the concept of cascade sensitivity, we extend the marginal fairness framework to capture how dependencies between covariates propagate the influence of protected attributes through the modeling pipeline. A numerical study and an empirical implementation using an auto insurance dataset demonstrate how the framework can be applied in practice.

Thanawat Sornwanee

We introduce a new microeconomics foundation of a specific type of competitive market equilibrium that can be used to study several markets with information asymmetry such as commodity market, credit market, and insurance market.

Maksym Komisarov

Мета роботи: удосконалити методику оцінювання ефективності системи протимінної діяльності. Метод дослідження: аналітичні, бальні методи, методи синтезу та формальної логіки. Результати дослідження: запропонована удосконалена методика є основою науково-методичного апарату для комплексного оцінювання ефективності функціонування системи протимінної діяльності, а також обґрунтування на його основі  практичних рекомендацій щодо підвищення ефективності функціонування системи ПМД, у яких комплексно ураховуватимуться особливості умов виконання заходів протимінної діяльності (зокрема, пріоритетності завдань розмінування та складності їх виконання) Теоретична цінність дослідження: аналіз впливу деяких факторів на протимінну діяльність в Україні та запропонований методичний підхід оцінки ефективності системи ПМД дозволить більш якісно виконувати заходи ПМД в державі. Тип статті: теоретичний.

Yurii Мykhailiuk, Oleh Bashnianyn

Мета роботи: розробка науково-методичного апарату вибору раціонального варіанта застосування прикордонного підрозділу для виконання завдань в умовах ймовірного сценарію розвитку загрози нелегальної міграції на ділянці відповідальності прикордонного загону. Метод: математичні моделі та методи. Результати дослідження: сформовано систему показників і критеріїв визначення стану протидії нелегальній міграції на ділянці відповідальності органу охорони державного кордону; обґрунтовано модель вибору раціонального варіанта застосування прикордонного підрозділу для виконання завдань в умовах ймовірного сценарію розвитку загрози нелегальної міграції на ділянці відповідальності прикордонного загону; розроблено методику прогнозування можливих сценаріїв обстановки на ділянці відповідальності прикордонного загону. Теоретична цінність дослідження: даний науково-методичний апарат дозволяє з достатньою адекватністю запровадити основні підходи щодо протидії нелегальній міграції на ділянці відповідальності прикордонного загону. Цінність дослідження: дозволяє приймати обґрунтовані рішення щодо вибору та подальшого формування раціонального варіанту застосування прикордонних підрозділів прикордонного загону у відповідності до конкретних сценаріїв розвитку обстановки, що дозволить гарантувати забезпечення ефективної протидії нелегальній міграції на конкретних ділянках відповідальності. Майбутні дослідження: у ході подальших досліджень доцільно опрацювати рекомендації штабу прикордонного загону щодо ефективної протидії нелегальній міграції на ділянці відповідальності.

Claudia Ceci, Katia Colaneri

We investigate the optimal investment-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques we convert the original optimization problem involving different filtrations, into an equivalent stochastic control problem under the observation filtration only, the so-called separated problem. The Markovian structure of the separated problem allows us to apply a classical approach to stochastic optimization based on the Hamilton-Jacobi-Bellman equation, and to provide explicit formulas for the value function and the optimal investment-reinsurance strategy. We finally discuss some comparisons between the optimal strategies pursued by a partially informed insurer and that followed by a fully informed insurer, and we evaluate the value of information using the idea of indifference pricing. These results are also supported by numerical experiments.

A Samuel Pottinger, Lawson Connor, Brookie Guzder-Williams et al.

Climate change not only threatens agricultural producers but also strains related public agencies and financial institutions. These important food system actors include government entities tasked with insuring grower livelihoods and supporting response to continued global warming. We examine future risk within the U.S. Corn Belt geographic region for one such crucial institution: the U.S. Federal Crop Insurance Program. Specifically, we predict the impacts of climate-driven crop loss at a policy-salient "risk unit" scale. Built through our presented neural network Monte Carlo method, simulations anticipate both more frequent and more severe losses that would result in a costly doubling of the annual probability of maize Yield Protection insurance claims at mid-century. We also provide an open source pipeline and interactive visualization tools to explore these results with configurable statistical treatments. Altogether, we fill an important gap in current understanding for climate adaptation by bridging existing historic yield estimation and climate projection to predict crop loss metrics at policy-relevant granularity.

Hassan Abdelrahman, Andrei Badescu, Radu Craiu et al.

Accurate loss reserving is crucial in Property and Casualty (P&C) insurance for financial stability, regulatory compliance, and effective risk management. We propose a novel micro-level Cox model based on hidden Markov models (HMMs). Initially formulated as a continuous-time model, it addresses the complexity of incorporating temporal dependencies and policyholder risk attributes. However, the continuous-time model faces significant challenges in maximizing the likelihood and fitting right-truncated reporting delays. To overcome these issues, we introduce two discrete-time versions: one incorporating unsystematic randomness in reporting delays through a Dirichlet distribution and one without. We provide the EM algorithm for parameter estimation for all three models and apply them to an auto-insurance dataset to estimate IBNR claim counts. Our results show that while all models perform well, the discrete-time versions demonstrate superior performance by jointly modeling delay and frequency, with the Dirichlet-based model capturing additional variability in reporting delays. This approach enhances the accuracy and reliability of IBNR reserving, offering a flexible framework adaptable to different levels of granularity within an insurance portfolio.