COVID-19 crisis and resilience: challenges for the insurance sector

The main role of the insurance sector is the coverage of risks through pooling techniques. Against the payment of a premium, the insurance company compensates for unexpected losses, including catastrophic events and pandemics. However, differently from a catastrophic event, the COVID-19 pandemic has highlighted that the global impact on economic and financial activities is highly correlated. The insurance sector itself has been strongly affected both by the exponential growth of claims in the life and non-life sectors and by the negative impact on financial activities. Past experiences in pandemic risk management have been unsuccessful. This paper retraces the instruments issued following the past pandemics and tries to reflect on how the insurance sector can implement innovative solutions to support post-pandemic resilience

Longevity risk and economic growth in sub-populations: evidence from Italy

Forecasting mortality is still a big challenge for Governments that are interested in reliable projections for defining their economic policy at local and national level. The accuracy of mortality forecasting is considered an important issue for longevity risk management. In the literature, many authors have analyzed the long-run relationship between mortality evolution and socioeconomic variables, such as economic growth, unemployment rate or educational level. This paper investigates the existence of a link between mortality and real gross domestic product per capita (GDPPC) over time in the Italian regions. Empirical evidence shows the presence of a relationship between mortality and the level of real GDPPC (and not its trend). Therefore, we propose a multi-population model including the level of real GDPPC and we compare it with the Boonen–Li model (Boonen and Li in Demography 54:1921–1946, 2017). The validity of the model is tested in the out-of-sample forecasting experiment

Neural Networks for quantile claim amount estimation: aq auntile regression approach

In this paper, we discuss the estimation of conditional quantiles of aggregate claim amounts for non-life insurance embedding the problem in a quantile regression framework using the neural network approach. As the first step, we consider the quantile regression neural networks (QRNN) procedure to compute quantiles for the insurance ratemaking framework. As the second step, we propose a new quantile regression combined actuarial neural network (Quantile-CANN) combining the traditional quantile regression approach with a QRNN. In both cases, we adopt a two-partmodel scheme where we fit a logistic regression to estimate the probability of positive claims and the QRNN model or the Quantile-CANN for the positive outcomes. Through a case study based on a health insurance dataset, we highlight the overall better performances of the proposed models with respect to the classical quantile regression one. We then use the estimated quantiles to calculate a loaded premium following the quantile premium principle, showing that the proposed models provide a better risk differentiation

Hygienization performances of innovative sludge treatment solutions to assure safe land spreading

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Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment : a review

Quantitative image analysis techniques have gained an undeniable role in several fields of research during the last decade. In the field of biological wastewater treatment (WWT) processes, several computer applications have been developed for monitoring microbial entities, either as individual cells or in different types of aggregates. New descriptors have been defined that are more reliable, objective, and useful than the subjective and time-consuming parameters classically used to monitor biological WWT processes. Examples of this application include the objective prediction of filamentous bulking, known to be one of the most problematic phenomena occurring in activated sludge technology. It also demonstrated its usefulness in classifying protozoa and metazoa populations. In high-rate anaerobic processes, based on granular sludge, aggregation times and fragmentation phenomena could be detected during critical events, e.g., toxic and organic overloads. Currently, the major efforts and needs are in the development of quantitative image analysis techniques focusing on its application coupled with stained samples, either by classical or fluorescent-based techniques. The use of quantitative morphological parameters in process control and online applications is also being investigated. This work reviews the major advances of quantitative image analysis applied to biological WWT processes.The authors acknowledge the financial support to the project PTDC/EBB-EBI/103147/2008 and the grant SFRH/BPD/48962/2008 provided by Fundacao para a Ciencia e Tecnologia (Portugal)

Nutraceutical therapies for atherosclerosis

Atherosclerosis is a chronic inflammatory disease affecting large and medium arteries and is considered to be a major underlying cause of cardiovascular disease (CVD). Although the development of pharmacotherapies to treat CVD has contributed to a decline in cardiac mortality in the past few decades, CVD is estimated to be the cause of one-third of deaths globally. Nutraceuticals are natural nutritional compounds that are beneficial for the prevention or treatment of disease and, therefore, are a possible therapeutic avenue for the treatment of atherosclerosis. The purpose of this Review is to highlight potential nutraceuticals for use as antiatherogenic therapies with evidence from in vitro and in vivo studies. Furthermore, the current evidence from observational and randomized clinical studies into the role of nutraceuticals in preventing atherosclerosis in humans will also be discussed

Clustering-based simultaneous forecasting of life expectancy time series through Long-Short Term Memory Neural Networks

In this paper, we apply a functional clustering method to the multivariate time series of life expectancy at birth of the female populations collected in the Human Mortality Database. We reconstruct the functional form of life expectancy from the available discrete observations and derive the curves through non-parametric smoothing. Once the clustering is realized, we perform the life expectancy simultaneous forecasting of the countries inside each cluster implementing a multivariate Long-Short Term Memory neural network. Although functional clustering has already been used in the actuarial literature, in this work it is applied for the first time to the study of life expectancy. The originality of the work also lies in the combination of a functional clustering approach with simultaneous forecasting obtained through the Long-Short Term Memory. We point out that such a combination provides a more informative outlook of the evolution of life expectancy, allowing us to depict country-specific longevity consistently with acknowledged mortality profiles. The results show that the evolution of developed countries follows a homogeneous pattern and supports the persisting homogeneity within the high longevity cluster over time. Moreover, we find a remarkable cross-country heterogeneity in the medium-low longevity cluster. By exploiting the cluster information, we improve the simultaneous forecasting of life expectancy time series using Long Short Term Memory neural networks and compare the error forecast of our approach with those of the classical VAR model, showing a better performance of the former when considering the cluster average errors

Network analysis of pension funds investments

In this paper, we analyze the Italian pension funds and their declared benchmarks, which are market indexes. Within this perspective, the amounts invested in accord to the declared benchmarks can be analyzed like as a portfolio of benchmarks. We aim at understanding whether the pension funds investments are in line with the optimal portfolios which can be built through the declared benchmarks. To achieve the results, we set up a portfolio optimization problem building two networks of pension funds: one based on the (Pearson) correlation, and the other measuring the tail correlation. For each network, we use the local clustering coefficients to describe the level of connectivity, and we insert it in the risk function. This approach allows us to consider the network measures directly in the portfolio optimization model. We compare the results with the classical Markowitz setting, and we find a new efficient frontier overperforming the Markowitz one. A comparison among the performances of pension funds and their declared portfolio of benchmarks is also reported