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OBJECTIVE: The integration of the health insurance fund pool may threaten the sustainability of the fund by increasing its expenditures through the exacerbation of the moral hazard of participations. The purpose of this paper is to assess and predict the impact of the single pool reform of China's Urban Employee Basic Medical Insurance (UEBMI) on the expenditure and sustainability of the health insurance fund. METHODS: In this paper, we consider the pilot implementation of the single pool reform in some provinces of China as a quasi-natural experiment, and develop a staggered DID model to assess the impact of the single pool reform on medical reimbursement expenditure. Based on the results, an actuarial model is developed to predict the impact on the accumulated balance of China's health insurance fund if the single pool reform is continued. RESULTS: We found that the medical reimbursement expenditure would increase by 66.4% per insured person after the unified provincial-level pool reform. There is individual heterogeneity in the effects of the unified single pool reform on medical reimbursement expenditure, and the reimbursement expenditure of retired elderly has the largest increase. If the unified single pool reform is gradually promoted, the current and accumulated balance of the UEBMI pooling fund would have gaps in 2031 and 2042, respectively. CONCLUSION: We verified that a larger fund pool will bring unreasonable growth of fund expenditures, which will threaten the sustainable development of health insurance. To minimize the impact of the unified single pool reform on the sustainability of the health insurance fund, we suggest strengthening the monitoring of moral hazard behavior, promoting the delayed retirement system, and encouraging childbearing.

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BACKGROUND: Liver transplantation is the gold standard treatment for end-stage liver disease. This study evaluates post-transplantation survival compared with the general population by quantifying standardized mortality ratios in a nested case-control study. METHODS: Controls were noninstitutionalized United States inhabitants from the National Longitudinal Mortality Study. Cases underwent liver transplantation from 1990 to 2007 identified through the Organ Procurement and Transplantation Network database. Propensity matching (5:1, nearest neighbor, caliper 0.1) identified controls based on age, sex, race, and state. The primary endpoint was 10-year survival. RESULTS: 62,788 cases were matched to 313,381 controls. The overall standardized mortality ratio was 2.46 (95% CI â€‹= â€‹2.44-2.48). The standardized mortality ratio was higher for males (2.59 vs. 2.25) and Hispanic patients (4.80). Younger patients and those transplanted earlier (1990-1995) had higher standardized mortality ratios. CONCLUSIONS: Liver recipients have a standardized mortality ratio 2.46 times higher than the general population. Long-term mortality has declined over time.

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Calling attention to the growing intersection between the insurance and technology sectors-or 'insurtech'-this article is intended as a bat signal for the interdisciplinary fields that have spent recent decades studying the explosion of digitization, datafication, smartification, automation, and so on. Many of the dynamics that attract people to researching technology are exemplified, often in exaggerated ways, by emerging applications in insurance, an industry that has broad material effects. Based on in-depth mixed-methods research into insurance technology, I have identified a set of interlocking logics that underly this regime of actuarial governance in society: ubiquitous intermediation, continuous interaction, total integration, hyper-personalization, actuarial discrimination, and dynamic reaction. Together these logics describe how enduring ambitions and existing capabilities are motivating the future of how insurers engage with customers, data, time, and value. This article surveys each logic, laying out a techno-political framework for how to orient critical analysis of developments in insurtech and where to direct future research on this growing industry. Ultimately, my goal is to advance our understanding how insurance-a powerful institution that is fundamental to the operations of modern society-continues to change, and what dynamics and imperatives, whose desires and interests are steering that change. The stuff of insurance is far too important to be left to the insurance industry.

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Although many studies supported the use of actuarial risk assessment instruments (ARAIs) because they outperformed unstructured judgments, it remains an ongoing challenge to seek potentials for improvement of their predictive performance. Machine learning (ML) algorithms, like random forests, are able to detect patterns in data useful for prediction purposes without explicitly programming them (e.g., by considering nonlinear effects between risk factors and the criterion). Therefore, the current study aims to compare conventional logistic regression analyses with the random forest algorithm on a sample of N = 511 adult male individuals convicted of sexual offenses. Data were collected at the Federal Evaluation Center for Violent and Sexual Offenders in Austria within a prospective-longitudinal research design and participants were followed-up for an average of M = 8.2 years. The Static-99, containing static risk factors, and the Stable-2007, containing stable dynamic risk factors, were included as predictors. The results demonstrated no superior predictive performance of the random forest compared with logistic regression; furthermore, methods of interpretable ML did not point to any robust nonlinear effects. Altogether, results supported the statistical use of logistic regression for the development and clinical application of ARAIs.

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Taking the aging population of Nanjing as an example,through the method of constructing actuarial models,this paper predicts and analyzes the impact of the reform of outpatient mutual aid security mechanism on the income and expenditure of the overall planning funds of workers'medical insurance in Nanjing during 2021-2035,and simulates the impact of policies such as delaying retirement,expanding financing and reforming payment methods on improving the sustainability of medical insurance funds.Based on this,it proposes policy suggestions.The study finds that under the current outpatient mutual aid security plan,by 2032,the overall planning funds of workers'medical insurance in Nanjing will face the risk of running out;compared with the current policy,implementing the same retirement age for men and women will delay the time point of accumulated deficits by one year;implementing the policy of expanding financing,after both employed and retired workers pay contributions to the overall planning funds,the overall planning funds will eliminate the risk of deficits in the medium term;implementing the policy of reforming payment methods can also make the overall planning funds sustainable in the medium term.Therefore,it is concluded that the reform of outpatient mutual aid security implemented in the context of aging population may lead to payment pressure on the overall planning funds.In the long run,a combination of various supporting policies is needed to achieve sustainable development of the overall planning funds.

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Much attention in biogerontology is paid to the deceleration of mortality rate increase with age by the end of a species-specific lifespan, e.g. after ca. 90 years in humans. Being analyzed based on the Gompertz law µ(t)=µ0e^γt with its inbuilt linearity of the dependency of lnµ on t, this is commonly assumed to reflect the heterogeneity of populations where the frailer subjects die out earlier thus increasing the proportions of those whose dying out is slower and leading to decreases in the demographic rates of aging. Using Human Mortality Database data related to France, Sweden and Japan in five periods 1920, 1950, 1980, 2018 and 2020 and to the cohorts born in 1920, it is shown by LOESS smoothing of the lnµ-vs-t plots and constructing the first derivatives of the results that the late-life deceleration of the life-table aging rate (LAR) is preceded by an acceleration. It starts at about 65 years and makes LAR at about 85 years to become 30% higher than it was before the acceleration. Thereafter, LAR decreases and reaches the pre-acceleration level at ca. 90 years. This peculiarity cannot be explained by the predominant dying out of frailer subjects at earlier ages. Its plausible explanation may be the acceleration of the biological aging in humans at ages above 65-70 years, which conspicuously coincide with retirement. The decelerated biological aging may therefore contribute to the subsequent late-life LAR deceleration. The biological implications of these findings are discussed in terms of a generalized Gompertz-Makeham law µ(t) = C(t)+µ0e^f(t).

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The aim of this paper is to track the whereabouts of basic old-age insurance (BOI) contribution subsidies. Little research has paid attention to the misalignment between the implementation objects of subsidy policies and the actual beneficiaries. In recent years, local governments in China have followed a contribution subsidy policy to help persons with disabilities (PWDs) with lower life expectancies. This study applied an actuarial model to calculate the leakage rate of the BOI contribution subsidy. The primary data are from the life tables of the entire population and PWDs in K province in China in 2015. It was found that, under different booking interest rates, 35.27%-61.26% of the contribution subsidy for PWDs in the individual account will be inherited by their heirs, and under different discount rates, 18.21%-68.74% in the pooling account will be enjoyed by non-disabled participants. The policy of BOI contribution subsidy for PWDs failed to account for the shorter average life expectancy and the lower average income of PWDs and would result in a welfare loss for PWDs. Therefore, it was suggested that it should be replaced by a non-contributory pension policy for PWDs.

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BACKGROUND: Because of a change of government, the Colombian Ministry of Health and Social Protection is in the process of presenting a structural reform for the General System of Social Security in Health (GSSSH), in order to implement a 'preventive and predictive health model'. However, it will always be relevant to review and analyze the fiscal implications of any proposed public policy program, to protect financial sustainability and to promote the better functioning of the system in question. METHODS: To contribute to this topic, we have calculated, using a financial-actuarial approach, the loss ratio for the years 2017 to 2021 for the Capitation Payment Unit (CPU) for all the Health-Promoting Entities (HPE) for both contributory and subsidized schemes. This information, derived from public reports available on the official website of the National Health Superintendency, allows us to estimate the financial burden of the institutions that guarantee access to and provision of health services and technologies in Colombia. RESULTS: The study shows that close to half of the HPEs in Colombia (which represent 11.6 million affiliates) have CPU loss ratios of more than 100% for the year 2021, evidencing insufficient resources for the operation of health insurance. CONCLUSIONS: Finally, we propose some policy recommendations regarding the strengthening of informed decision-making to allow the healthy financial sustainability of the Colombian GSSSH.

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El análisis de supervivencia es un método estadístico que valora el tiempo entre un evento inicial (inclusión del sujeto en el estudio) y uno final, que sucede cuando este presenta una característica definida con anterioridad (evento). Su objetivo es estimar, teniendo en cuenta la variable tiempo, la probabilidad de que ocurra un suceso determinado. Tiene la particularidad de aceptar tiempos incompletos de participación y asumir que todos los factores implicados en el estudio son homogéneos. Existen varios métodos para calcular la probabilidad de supervivencia; los más utilizados son los de Kaplan-Meier y el actuarial (AU)

Survival analysis is a statistical method that assesses the time between an initial event (inclusion of the subject in the study) and a final event, which occurs when the subject presents a previously defined characteristic. Its objective is to estimate, taking into account the time variable, the probability of a certain event occurring. It has the particularity of accepting incomplete participation times and assuming that all the factors involved in the study are homogeneous. There are several methods to calculate the probability of survival, the most used are the Kaplan-Meier and the actuarial (AU)

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Algorithmic predictions are promising for insurance companies to develop personalized risk models for determining premiums. In this context, issues of fairness, discrimination, and social injustice might arise: Algorithms for estimating the risk based on personal data may be biased towards specific social groups, leading to systematic disadvantages for those groups. Personalized premiums may thus lead to discrimination and social injustice. It is well known from many application fields that such biases occur frequently and naturally when prediction models are applied to people unless special efforts are made to avoid them. Insurance is no exception. In this paper, we provide a thorough analysis of algorithmic fairness in the case of insurance premiums. We ask what "fairness" might mean in this context and how the fairness of a premium system can be measured. For this, we apply the established fairness frameworks of the fair machine learning literature to the case of insurance premiums and show which of the existing fairness criteria can be applied to assess the fairness of insurance premiums. We argue that two of the often-discussed group fairness criteria, independence (also called statistical parity or demographic parity) and separation (also known as equalized odds), are not normatively appropriate for insurance premiums. Instead, we propose the sufficiency criterion (also known as well-calibration) as a morally defensible alternative that allows us to test for systematic biases in premiums towards certain groups based on the risk they bring to the pool. In addition, we clarify the connection between group fairness and different degrees of personalization. Our findings enable insurers to assess the fairness properties of their risk models, helping them avoid reputation damage resulting from potentially unfair and discriminatory premium systems.

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Objective: As China's population aging process accelerates, the expenditure of China's basic medical insurance fund for employees may increase significantly, which may threaten the sustainability of China's basic medical insurance fund for employees. This paper aims to forecast the future development of China's basic medical insurance fund for employees in the context of the increasingly severe aging of the population. Methods: This paper taking an empirical study from Shanghai as an example, constructs an actuarial model to analyze the impact of changes in the growth rate of per capita medical expenses due to non-demographic factors and in the population structure on the sustainability of the basic medical insurance fund for employees. Results: Shanghai basic medical insurance fund for employees can achieve the goal of sustainable operation in 2021-2035, with a cumulative balance of 402.150-817.751 billion yuan in 2035. The lower the growth rate of per capita medical expenses brought about by non-demographic factors, the better the sustainable operation of the fund. Conclusion: Shanghai basic medical insurance fund for employees can operate sustainably in the next 15 years, which can further reduce the contribution burden of enterprises, which lays the foundation for improving the basic medical insurance treatment for employees.

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Survival analysis is a statistical method that assesses the time between an initial event (inclusion of the subject in the study) and a final event, which occurs when the subject presents a previously defined characteristic. Its objective is to estimate, taking into account the time variable, the probability of a certain event occurring. It has the particularity of accepting incomplete participation times and assuming that all the factors involved in the study are homogeneous. There are several methods to calculate the probability of survival, the most used are the Kaplan-Meier and the actuarial.

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INTRODUCTION: In offending populations, prevalence rates of mental disorders are much higher than in the general population. Nevertheless, it is unclear whether mental disorders can improve the prediction of recidivism beyond actuarial risk assessment tools. METHODS: The present prospective-longitudinal study was conducted between 2001 and 2021 and included 1066 men convicted of sexual offenses in Austria. All participants were evaluated with actuarial risk assessment tools for the prediction of sexual and violent recidivism and the Structured Clinical Interview for Axis I and Axis II disorders. Sexual and violent reconvictions were assessed. RESULTS: Exhibitionism and an exclusive pedophilia showed the strongest correlations with sexual recidivism in the total sample. In the child related offense subsample additionally a narcissistic personality disorder was correlated with sexual recidivism. The strongest correlation with violent recidivism was found for an antisocial and borderline personality disorder. None of the mental disorders could improve the prediction of recidivism beyond actuarial risk assessment tools. CONCLUSION: Common current actuarial risk assessment tools revealed good predictive accuracy in men convicted of sexual offenses. With few exceptions mental disorders were only weakly associated with recidivism, suggesting that there is no direct link between mental disorders and violent and sexual reoffending. Mental disorders should nevertheless be considered in treatment issues.

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The Capitation Payment Unit (CPU) financing mechanism constitutes more than 70% of health spending in Colombia, with a budget allocation of close to 60 trillion Colombian pesos for the year 2022 (approximately 15.7 billion US dollars). This article estimates actuarially, using modern techniques, the CPU for the contributory regime of the General System of Social Security in Health in Colombia, and compares it with what is estimated by the Ministry of Health and Social Protection. Using freely available information systems, by means of statistical copulas functions and artificial neural networks, pure risk premiums are calculated between 2015 and 2021. The study concludes that the weights by risk category are systematically different, showing historical pure premiums surpluses in the group of 0-1 years and deficits (for the regions normal and cities) in the groups over 54 years of age.

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PURPOSE: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy. METHODS: CT, RD images and clinical parameters were obtained from 314 retrospectively-collected patients (training set) and 35 prospectively-collected patients (test-set-1) who were diagnosed with lung cancer and received radical radiotherapy in the dose range of 50 Gy and 70 Gy. Another 194 (60 Gy group, test-set-2) and 158 (74 Gy group, test-set-3) patients from the clinical trial RTOG 0617 were used for external validation. A ResNet architecture was used to develop a prediction model that combines CT and RD features. Thereafter, the CT and RD weights were adjusted by using 40 patients from test-set-2 or 3 to accommodate cohorts with different clinical settings or dose delivery patterns. Visual interpretation was implemented using a gradient-weighted class activation map (grad-CAM) to observe the area of model attention during the prediction process. To improve the usability, ready-to-use online software was developed. RESULTS: The discriminative ability of a baseline trained model had an AUC of 0.83 for test-set-1, 0.55 for test-set-2, and 0.63 for test-set-3. After adjusting CT and RD weights of the model using a subset of the RTOG-0617 subjects, the discriminatory power of test-set-2 and 3 improved to AUC 0.65 and AUC 0.70, respectively. Grad-CAM showed the regions of interest to the model that contribute to the prediction of RP. CONCLUSION: A novel deep learning approach combining CT and RD images can effectively and accurately predict the occurrence of RP, and this model can be adjusted easily to fit new cohorts.

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Actuarial senescence, the decline of survival with age, is well documented in the wild. Rates of senescence vary widely between taxa, to some extent also between sexes, with the fastest life histories showing the highest rates of senescence. Few studies have investigated differences in senescence among populations of the same species, although such variation is expected from population-level differences in environmental conditions, leading to differences in vital rates and thus life histories. We predict that, within species, populations differing in productivity (suggesting different paces of life) should experience different rates of senescence, but with little or no sexual difference in senescence within populations of monogamous, monomorphic species where the sexes share breeding duties. We compared rates of actuarial senescence among three contrasting populations of the Atlantic puffin Fratercula arctica. The dataset comprised 31 years (1990-2020) of parallel capture-mark-recapture data from three breeding colonies, Isle of May (North Sea), Røst (Norwegian Sea) and Hornøya (Barents Sea), showing contrasting productivities (i.e. annual breeding success) and population trends. We used time elapsed since first capture as a proxy for bird age, and productivity and the winter North Atlantic Oscillation Index (wNAO) as proxies for the environmental conditions experienced by the populations within and outside the breeding season, respectively. In accordance with our predictions, we found that senescence rates differed among the study populations, with no evidence for sexual differences. There was no evidence for an effect of wNAO, but the population with the lowest productivity, Røst, showed the lowest rate of senescence. As a consequence, the negative effect of senescence on the population growth rate (λ) was up to 3-5 times smaller on Røst (Δλ = -0.009) than on the two other colonies. Our findings suggest that environmentally induced differences in senescence rates among populations of a species should be accounted for when predicting effects of climate variation and change on species persistence. There is thus a need for more detailed information on how both actuarial and reproductive senescence influence vital rates of populations of the same species, calling for large-scale comparative studies.

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This study examined the discrimination and calibration properties of the Violence Risk Appraisal Guide-Revised (VRAG-R) within a large subset of the population of 574 individuals who had been found Not Criminally Responsible on Account of Mental Disorder (NCRMD) in Alberta. The VRAG-R was scored on all individuals identified via The Alberta NCR Project database from every file that contained sufficient relevant information and recidivism data were obtained via official criminal records. The VRAG-R demonstrated strong discrimination properties for general and violent recidivism over 5-year, 10-year, and global follow-ups. Calibration analyses, however, indicated that the VRAG-R substantially over estimated violence risk and that there was poor agreement between expected and observed recidivism rates for this population. When examined in the male subsample, these issues remained but to a lesser degree; examination of VRAG-R discrimination and calibration for females was not possible due to a lack of recidivists. Results indicated strong discrimination but poor calibration properties of the VRAG-R in this NCRMD population. Overall, the results support the use of the VRAG-R within a population of persons found NCRMD when employed in tandem with other measures as part of a comprehensive psychological risk assessment.

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Abstract This article presents the Brazilian private insurance market's actuarial life tables, BR- EMS 2021. Using Bayesian inference on the parameters of the Heligman- Pollard law of mortality and data from 23 insurance groups over 15 years, totaling 3.5 billion registers, the data were corrected through a two hidden-layer neural network. The resulting tables show that the insured population exhibits lower mortality rates than the general Brazilian population, even lower than the national populations of well-developed countries such as the USA. Moreover, besides the expected gender gap in mortality rates, there is a clear distance between the death and survivorship insurance coverage groups. Likewise, the insured population characteristics mitigate well-known regional structural discrepancies in the Brazilian population, indicating that being part of the selected population of insured individuals is thus associated with a more effective protection against death than other outstanding factors such as geographic region of residence.

Resumo Este artigo apresenta as tábuas de vida do mercado de seguros privados brasileiro, BR-EMS 2021. Os dados obtidos de 23 grupos seguradores ao longo de 15 anos, totalizando 3,5 bilhões de registros, foram corrigidos por meio de rede neural com duas camadas ocultas. Usando a inferência bayesiana para estimar os parâmetros sob a lei de mortalidade Heligman-Pollard, as tábuas obtidas mostram que a população segurada apresenta probabilidades de morte mais baixas do que a população brasileira em geral e até mesmo em relação a populações nacionais de países desenvolvidos, como os EUA. Além da esperada diferença de gênero nas taxas de mortalidade, há uma clara distância entre as probabilidades de morte dos grupos de cobertura de risco e cobertura de sobrevivência. Da mesma forma, é demonstrado que as tábuas regionais da população segurada não apresentam as discrepâncias regionais conhecidas no Brasil, indicando que fazer parte da população selecionada de segurados está associado a um fator de proteção mais eficaz do que outros fatores, como a região geográfica de residência.

Resumen Este artículo presenta las tablas de vida del mercado de seguros privados brasileño, BR-EMS 2021. Los datos, obtenidos de 23 grupos de seguros a lo largo de 15 años, totalizando 3,5 mil millones de registros, fueron corregidos usando una red neuronal con dos capas ocultas. Mediante la inferencia bayesiana para estimar los parámetros bajo la ley de mortalidad de Heligman-Pollard, las tablas obtenidas muestran que la población asegurada tiene tasas de mortalidad más bajas que la población general brasileña e incluso más bajas que las poblaciones nacionales de países desarrollados, como los Estados Unidos de Norteamérica. Además de la diferencia de género esperada en las tasas de mortalidad, hay una clara distinción entre las tablas de grupos de cobertura de riesgo y cobertura de sobrevivientes. Asimismo, se demuestra que las tablas regionales de población asegurada no presentan las conocidas discrepancias estructurales regionales en Brasil, lo que indica que participar de la población de asegurados está asociado con una protección contra la muerte más efectiva que otros factores como la región geográfica de residencia.

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The foundation of the insurance business is built on data, the latter being one of the most valuable assets of any insurer. In fact, the risk structure to which an insurance company is exposed can actually be deduced by reviewing its customer database. It is not surprising, therefore, that access to real insurance datasets is very limited. This paper introduces and describes a dataset corresponding to a cross-section extraction of a real life-risk insurance portfolio. The dataset contains information on 76,102 policies and a total of 15 variables, including the capital at risk, the genders and dates of birth of the insured, and the effective and renewal dates of their policies. This dataset can be used both in teaching and in research. Combined with external life tables, the data available in the dataset can be used to build and compare pricing systems, to evaluate marketing strategies, in portfolio analysis, for calculations required by Solvency II regulations or for market benchmarking analysis. For example, the data from this dataset have been used in Pavía and Lledó [1] to compare the classic pricing methodology based on annual life tables with a new pricing methodology based on life tables with less than annual periodicity Pavía and Lledó [2], specifically quarterly, and in Lledó et al. to demonstrate the impact that using a new methodology to manage catastrophic risks in life insurance would have in terms of solvency capital requirements.

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This article provides an overview of all papers published on the special issue, Advances in Actuarial Science and Quantitative Finance. The special issue is intended to collect articles that reflect the latest development and emerging topics in these closely related two areas. Topics included in this special issue range from actuarial and risk theory, to optimal control for finance and insurance, to statistical inferences of financial and insurance models, to pricing, valuation and reserving.