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Within the insurance field, the digital revolution has enabled the collection and storage of large quantities of information. This era is referred to as “big data”, since the great uncertainty to be modelled is too complex for traditional data processing techniques. For insurance purposes, big data refers to unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling. The use of artificial intelligence (AI) in actuarial science is a rapidly growing area that is significantly impacting the insurance industry. This integration of technology involves both machine learning (ML) algorithms and data analysis to help actuaries develop more accurate risk assessments and predictions. With a larger possibility of accessing huge sets of data, actuaries can leverage AI to automate underwriting and claims processing, improve customer experience and provide valuable insights to insurers to perfect decision-making strategies. In this way, AI has the potential to transform traditional actuarial methodologies and provide customized solutions for individual clients. In particular, the field of insurance has greatly benefited from the application of data science. With the use of data analytics, insurance companies can now develop predictive models to better manage risk, underwrite policies more accurately and determine valid pricing. Data science is also used to automate specific procedures such as claims processing, fraud detection, and customer retention. Insurance companies may use data science to identify and build new products that better match clients’ needs and preferences. In addition, the accessibility of data can improve operational efficiency by identifying areas of improvement and revenue-generating opportunities. Overall, the use of data science is quickly becoming a crucial aspect of the insurance industry, helping insurers improve their decision making, create new products and services and enhance custo

In this article, we investigate the occurrence of defects in integrated circuit fabrication and show how spatial analysis can be effective in grasping and representing spatial regularities in defect patterns on the silicon supports, called wafers, used to produce microchips. Defects occurring on the wafer surface are the main cause of yield loss in the semiconductor industry; hence, to promptly detect an excess of defects and identify their spatial structure is crucial to the entire fabrication process. To address this hard-to-solve problem, this article proposes a concatenation of different methods, namely, a control chart, a clustering algorithm, and a graphical tool. First, a control chart based on the p-value of an appropriate test recognizes spatially structured defects on the wafer area. Then a clustering procedure grounded on the minimum spanning tree algorithm is adopted to identify those regions more prone to defect occurrences. Finally, alpha-shapes are employed to display their shape effectively. The suggested procedure proves to be extremely fast and effective, allowing its implementation in-line during the fabrication process. This provides a great advantage in modern microelectronics where items tend to be highly specialized and often produced in small lots. In particular, due to the Monte Carlo nature of the procedure, the control chart proposed hereafter does not require gold standard data to be set. This is particularly advantageous for small lot production, which is typically limited in time and does not permit to collect long time series of the charting statistics.

Worldwide, thousands of people die annually in highway-related crashes and millions are injured. Hence, car wrecks have very high direct social costs but also relevant indirect economic effects such as an adverse impact on the burden of hospitalization and an increased health expenditure. The analysis of car crash data has long been used as a basis for influencing highway and vehicle designs but also, and perhaps more importantly, to support local authorities in allocating resources aimed at improving road safety and making political decisions to mitigate road risks in the most exposed areas. In this paper, we show how a range of information collected from open data sources concerning the structure of the road network (road typology, traffic lights, pedestrian crossings, etc.), socio-demographical dimensions and crash history can be proficiently used for this aim. We adopt a dynamic Zero Inflated Poisson (ZIP) regression model to define two indexes. The first index, derived from the counting component of the ZIP model, measures how prone to crash risk a segment is. The other, derived by the zero component of the ZIP model, represents a measure of the likelihood of segments to not be exposed to crashes. Focussing on the city of Milan (Northern Italy), we found that the most relevant determinant of road risk proneness is crash history and that structural characteristics of the road are much more relevant than demographic information. Finally, we show how this information can be spatialized to produce maps of crash proneness and predict future spatial risk indexes.

Process capability indices are routinely used to estimate the mean-variability performance of industrial products with respect to both targets and specification limits. However, when the target variable is defined over a planar surface of a manufact, it is relevant to assess the capability of the production process locally, that is, at any spatial location of the surface, in particular if the manufact has to be split into pieces to obtain single production items. In this article, focusing on the specification introduced by Clements [Qual Prog., 22, 95–100], we suggest an approach based on additive quantile models to estimate, in a Bayesian paradigm, the index locally. We demonstrate its use in the context of the etching phase of the integrated circuit fabrication process. Since capability of etching processes is typically assessed for batches of wafers, we also propose two algorithms based on resampling to perform local capability analysis at the lot level.

The monitoring of production processes on a planar surface typically involves sampling network to gather information about the status of the process. In order to save time and money, when the process goes into a stable status it might be appropriate to reduce the dimension of the sampling grid. In some cases, the allocation of a new network of smaller dimension is not free of constraints and it might be necessary the selection of a subgrid extracted from the original network. Discussion is focused on some recent methods used to achieve this aim. Possible extensions to consider jointly tabu search algorithm and co-kriging models is reported.

This paper highlights advantages and drawbacks due to the use of portable and low-cost devices for aerosol sampling, showing their performances during an aerosol monitoring campaign with the parallel use of the gravimetric sampling reference method and a cascade impactor. A specific monitoring campaign was held running all instruments in parallel in indoor, confined, and outdoor environments characterized by local emission sources or particulate matter background concentra-tions. PM2.5 concentrations were used to compare data emerging from the different instruments adopted. Significant underestimation of PM2.5 emerged when comparing data coming from optical sensors with those estimated by the cascade impactor, whose data resulted in being coherent with gravimetric determination, integrated over the same sampling time. A cause–effect relationship between PM2.5 concentrations and specific emission sources was found when observing the daily patterns of all the real-time sampling devices. It emerged that optical devices are useful for detecting concentration trends, the presence of peak values, or changes in the background value, even if with limited accuracy and precision. The comparison with particle size distributions obtained by the cascade impactor data allowed us to define which particle sizes are not detected by different optical devices, evidencing a low representativeness of optical low-cost sensors for health exposure measurements. The correlations among the specific particle size fractions detected by the cascade im-pactor and their specific emission sources were particularly high for car emissions in a semi-confined garage area.

Process capability indices are widely used to check quality standards both at the production level and for business activity. They consider the location and the deviation from specification limits and targets. The literature contains many contributions on this topic both in the univariate and the multivariate context. Motivated by a real semiconductor case study, we discuss the role of rational subgroups and the challenge they present in the computation of capability indices, especially when data refer to lots of products. In addition, our context involves a mix of problems: unilateral specification limit, nonsymmetric distribution of the data, evidence of data from a mixture of distributions, and the need to filter one component of the mixture. After solving the previous issues and because of the peculiar characteristics of semiconductor processes based on the so called “wafers,” we contribute to the literature a proposal on how to compute capability indices in the case of heteroscedastic spatial processes. With a generalized additive model, we show that it is possible to estimate a capability surface that allows the identification of regions expected to not be fully compliant with the desired quality standards.

In general insurance, measuring the uncertainty of future loss payments and estimating the claims reserve are primary goals of actuaries. To deal with these tricky tasks, a broad literature is available on deterministic and stochastic approaches, most of which aims at straightforwardly modelling the overall claims reserve. In this paper by an extended, very general and reproducible case-study, we analyze the reserving process by attributing to each cell of the lower part of the run-off triangle a Compound mixed Poisson Process, calibrated upon both the numbers of claims and future average costs and considering as well the dependence among incremental claims. We provide analytically the moments of both incremental payments and the total reserve. Furthermore, we accordingly consider the probability distribution of the claims reserve, which is necessary for the assessment of the Risk Reserve capital requirement in a Solvency II framework. To test the impact of the model under different scenarios, insurers and lines of business, the case study is thoroughly analysed by exploiting the Fisher-Lange average cost method.

This paper introduces a special issue entirely devoted to the topic 'Statistics for Microelectronics'. The relevance of the theme for this journal is due to both the primary role that microelectronics has in many business activities of the modern society and to the complexity of the production process of integrated circuits that are obtained by several different steps performed on a wafer (i.e., a thin silicon slice of a few inches of diameter). Advanced statistical methods are necessary to monitor and improve this process because the yield of this industrial manufacturing is typically tiny and requires a very high precision. After a brief review of the semiconductor manufacturing process, we point out how statistical methodologies can contribute to this. Finally, we introduce the papers that discuss some key aspects of this subject. Copyright (c) 2013 John Wiley & Sons, Ltd.

Deposition of silicon dioxide (SiO2) is a critical step of integrated circuit manufacturing; hence it is monitored during the manufacturing process at a grid of points defined on the wafer area. Since collecting thickness measurements is expensive, it is a compelling issue to investigate how a sub grid can be identified. A strategy based on spatial prediction and simulating annealing is proposed to tackle the problem which proved to be effective when applied to a real process. A diagnostic device for monitoring the deposition process is also discussed which can be usefully adopted in the day-to-day activity by practitioners acting in process control of a microelectronics fab. © 2012 Elsevier B.V. All rights reserved.