Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework

The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks—a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay. In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR®, a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies’ data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a self-optimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal

Effect of Nanosized TiO2 on Nucleation and Growth of Cristobalite in Sintered Fused Silica Cores for Investment Casting

Sintered fused silica is often used for making sacrificial cores in investment castings of Ni superalloys. Their usage is fundamental in the manufacture of precise superalloy gas turbine components with complex internal cooling passages. In this study SiO2/ZrSiO4/TiO2 cores were prepared from fused silica powders with different grain size and zircon and TiO2 content by slip casting method. Green samples were sintered at 1230°C at various soaking time: from 0,5 to 10 hours. Thermomechanical and microstructural properties of optimized silica obtained by add of 1,5%wt of TiO2 to SiO2/ZrSiO4 composition have been investigated by three point bending tests, XRD and Hg porosimetric analysis. The influence of cristobalite content on thermal stability at high temperature was studied by an optical dilatometer. At temperature below 1200°C TiO2 appears to act as a phase transformation inhibitor reducing the transformation rate of fused silica to cristobalite at high temperatures. At higher temperature it speeds up the formation of cristobalite. A comparison with commercial silica cores made by injection moulding has been performed. A prototype core was obtained and an investment casting was performed on that

Relevant applications of Monte Carlo simulation in Solvency II

The definition of solvency for insurance companies, within the European Union, is currently being revised as part of Solvency II Directive. The new definition induces revolutionary changes in the logic of control and expands the responsibilities in business management. The rationale of the fundamental measures of the Directive cannot be understood without reference to probability distribution functions. Many insurers are struggling with the realisation of a so-called “internal model” to assess risks and determine the overall solvency needs, as requested by the Directive. The quantitative assessment of the solvency position of an insurer relies on Monte Carlo simulation, in particular on nested Monte Carlo simulation that produces very hard computational and technological problems to deal with. In this paper, we address methodological and computational issues of an “internal model” designing a tractable formulation of the very complex expectations resulting from the “market-consistent” valuation of fundamental measures, such as Technical Provisions, Solvency Capital Requirement and Probability Distribution Forecast, in the solvency assessment of life insurance companies. We illustrate the software and technological solutions adopted to integrate the Disar system—an asset–liability computational system for monitoring life insurance policies—in advanced computing environments, thus meeting the demand for high computing performance that makes feasible the calculation process of the solvency measures covered by the Directive

Relevant applications of Monte Carlo simulation in Solvency II

The definition of solvency for insurance companies, within the European Union, is currently being revised as part of Solvency II Directive. The new definition induces revolutionary changes in the logic of control and expands the responsibilities in business management. The rationale of the fundamental measures of the Directive cannot be understood without reference to probability distribution functions. Many insurers are struggling with the realisation of a so-called “internal model” to assess risks and determine the overall solvency needs, as requested by the Directive. The quantitative assessment of the solvency position of an insurer relies on Monte Carlo simulation, in particular on nested Monte Carlo simulation that produces very hard computational and technological problems to deal with. In this paper, we address methodological and computational issues of an “internal model” designing a tractable formulation of the very complex expectations resulting from the “market-consistent” valuation of fundamental measures, such as Technical Provisions, Solvency Capital Requirement and Probability Distribution Forecast, in the solvency assessment of life insurance companies. We illustrate the software and technological solutions adopted to integrate the Disar system—an asset–liability computational system for monitoring life insurance policies—in advanced computing environments, thus meeting the demand for high computing performance that makes feasible the calculation process of the solvency measures covered by the Directive

Variant of Rett Syndrome and CDKL5 gene: clinical and autonomic description of 10 cases

Rett syndrome (RTT) is a severe neurodevelopmental disorder affecting almost exclusively females. The Hanefeld variant, or early-onset seizure variant, has been associated with mutations in CDKL5 gene. AIMS: In recent years more than 60 patients with mutations in the CDKL5 gene have been described in the literature, but the cardiorespiratory phenotype has not been reported. Our aim is to describe clinical and autonomic features of these girls. METHODS: 10 girls with CDKL5 mutations and a diagnosis of Hanefeld variant have been evaluated on axiological and clinical aspects. In all subjects an evaluation of the autonomic system was performed using the Neuroscope. RESULTS: Common features were gaze avoidance, repetitive head movements and hand stereotypies. The autonomic evaluation disclosed eight cases with the Forceful breather cardiorespiratory phenotype and two cases with the Apneustic breather phenotype. CONCLUSIONS: The clinical picture remains within the RTT spectrum but some symptoms are more pronounced in addition to the very early onset of seizures. The cardiorespiratory phenotype was dominated by Forceful breathers, while Feeble breathers were not found, differently from the general Rett population, suggesting a specific behavioral and cardiorespiratory phenotype of the RTT the Hanefeld variant