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In the modern financial landscape, there is an increasing interest in using unconventional data sources for predicting Small and Medium Enterprises (SME) default. SMEs, indeed, have a pivotal role in the EU economy constituting 99.8% of all enterprises in the EU-28 non-financial business sector (European Commission (2019)). So far, credit scoring models have been built using only balance sheets indicators, although it has been demonstrated that smaller sized firms do not always comply with financial transparency. In this, work we explore the usage of corporate websites as a further source of information for detection of SMEs default (Crosato, Domenech, & Liberati, 2021). Working with unconventional data raises several challenges in terms of data retrieval, selection and cleaning. On the other hand, it allows to overcome the deterioration of the balance sheet indicators, that generally are published with 2 years of delay with respect to their reference time. We relied on our study on a sample of Spanish SMEs (about 900 firms) whose websites were scraped to obtain online indicators based on the HTML code, as in as in Blazquez and Domenech (2018). The online indicators, merged with balance sheet data, were then analyzed using Machine Learning algorithms (as Random Forest and LS-SVM) and Logistic Regression (as a benchmark) having as input variables both sets of information. Results show the improvement in terms of misclassification rate when the accounting information is augmented by website features. We also provide valuable insights into companies’ websites characteristics related with firms’ financial health. Finally, we show that online indicators are able to predict the future status of some uncommon firms whose accounting indicators would have led to the wrong prediction.

Research in innovation usually builds on conventional data such as balance sheets, surveys, patents, or product catalogs. This paper intends to explore unconventional data, specifically web-scraped data, as an information source for innovation studies, proposing a careful procedure to establish the veracity of the linkage between web-based data and firm-level information retrieved from conventional sources. The study regards a sample of Italian manufacturing small and medium enterprises active in 2016, comprehending both innovative and non-innovative firms. It is based on HTML tags, whilst most of the previous literature worked on the web-pages text and related semantics. Our paper provides evidence that the way HTML language is applied to build a corporate website unveils the capabilities of the owner firm, helping to distinguish innovative from non-innovative SMEs.

As recently stated by EUROSTAT, Small and Medium Enterprises (SMEs) play a crucial role in the economy of European Union (EU). This is particularly true for Italy that has one of the largest share of SMEs in the Euro zone. In such a context, the assessment of the Italian firms creditworthiness is a priority, especially in the early stage of a SMEs life. The aim of this work is to explore the effectiveness of the Feedforward Neural Network for Italian firms defaults prediction in comparison with standard linear models (Logistic and Probit). Our analysis considers the Italian manufacturing SMEs in a short time span (2016-2017). Results, obtained via two different sampling strategies, are not clear-cut.

The objective of credit scoring is to develop accurate rule of classification that aids to distinguish between good and bad clients. In this context, also Statistical Learning (SL) techniques have been explored, for building models that estimate the clients’ probability of insolvency. Although there are some encouraging results in literature, two main issues makes this classification task very hard: (i) high imbalance ratio between the two groups in the target variable and (ii) the effect of hyperparameter settings on overall performance. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a cost sensitive eXtreme Gradient Boosting (XGBoost) model. Experimental results reveal that the proposed solution is a promising starting point for future development

Small and Medium Enterprises (SMEs) are the backbone of the European Union (EU). They account for the majority of the value-added within the non-financial sector and employ two-thirds of the workforce. Therefore the assessment of the SMEs' creditworthiness is a significant issue within both financial and non-financial organizations. Recent works suggest that SMEs' default prediction is more complex than in large enterprises, and canonical models may fall short. At the same time, scholars pointed out similarities with the retail credit risk industry. In this context, black-box machine learning models are the standard, and they achieved remarkable performances. However, these models are less frequently used in enterprise credit scoring as they are essentially uninterpretable. This work aims to model SMEs' defaults using uninterpretable black-box models and compare them with the canonical models. We first explore these models' effectiveness for default prediction compared to standard models (Logistic, Probit, and Binary Generalized Extreme Value). Second, we overcome the lack of interpretability by using recent model-agnostic techniques such as Accumulated Local Effects and Shapley values. We use the dataset to estimate the models and predictions containing Italian Small and Medium Enterprises' information belonging to the manufacturing sector. The results suggest that the black-box models outperform the standard models used as the benchmark.

Millennials and Religion: Divergence or Continuity Compared to the Generation of their Parents? ·This paper deals with the relationship between Millennials and religion with the aim at determining whether this generation diverges from that of their mothers and fathers also from the point of view of religious identity and experience. The paper is divided into two parts: a literature review on the theme of generational differences in religious identity and experience with reference to the Italian context and a second part dedicated to data analysis. We analyze approximately 3,700 questionnaires on this issue completed by students attending the University of Milano-Bicocca.

In financial literature there are many contributions aim to measure default risk of credit borrowers. This work explores the application of a survival model to reach this purpose. The data were provided by a broker and it refers to Small and Medium Enterprises (SMEs) of the South Italy. In particular, we analysed the companies’ credit lines, in order to assess death rate of the credit intended as insolvency risk of the enterprises. The sample collects information about 5.322 credit lines, referred to the 2011. The paper attempts to direct an exploratory investigation about risk factors for default prediction, using financial and some non-financial information that are available from public sources. Among non standard risk factors that can prevent a lending decision, we focused the attention on quality-of-life indicators of the municipalities where the monitored Small and Medium Enterprises are located. Such indicators have been built considered several different sides of the local territory, that vary from geographical to socio-economic environment. They are used as explanatory variables in a set of survival models in order to verify the existence of significant correlation between location of the company and the default probability. We expect to show interesting evidences for the defaulted companies, although the efficiency of the environment has different impact to the companies’ performances that can be very variable.

In recent studies, there has been a growing interest toward tolerance and its implications in social and economic systems. So far, the openness of people to homosexuals or to foreign-born people has been considered the best indicator of tolerant attitudes. In this paper, we consider tolerance as a multi-faced phenomenon involving several different social domains. The aim is to provide some recommen- dations on how to develop a multidimensional index for tolerance in the case of survey’s items measured by Likert-scale. Our discussion relies on a case study about a student survey carried out at Milan-Bicocca University.

Customer Satisfaction management has been long studied as a cross-sectional phenomenon. Today, a shift in emphasis from a static to a dynamic approach appears to be a worthwhile change, although the design and monitoring of panel data is not simple to achieve. In our paper, we propose the usage of pseudo panels, which are less costly and easy to build with the available data for the firms. Our approach is based on a model-free technique: Dual Multiple Factor Analysis. The synthesis of the multivariate table is visualised on a common space that sheds light on customers' trajectories of satisfaction. A real case study of an Italian bank is illustrated. Results related to the dynamic profiling of the clients highlights highly different behaviours that warn the management of the future trends of the services.

Traditionally, customer’s ‘character’ was used as one of the main criteria in credit granting process (Three ‘C’s of Credit). Credit scoring (risk models targeted at screening individuals) replaced it with hard information, such as credit history and demographics. Recent studies reignited the interest in character/ personality by showing its relevance in modelling consumer indebtedness, and more general financial behaviour. Unfortunately, most literature in this field is mainly based on self-reported financial measures. We overcome this limitation by analysing a unique dataset of Italian bank customers that contains hard information about credit and investment performance, in addition to soft measures of personality, financial knowledge and preferences for different levels of customer support. The purpose of this work is twofold: first, we aim at detecting if there are different psychographic segments of banking customers based on personality traits and other soft information. We experiment with clustering and different kernels in Kernel Principal Components Analysis. The comparison indicates the presence of different clustered structures in the data but some partitions may be difficult to interpret from a managerial perspective. Second, we analyse the investment and credit performance of the different customer typologies. We comment on the model fit and predictive accuracy of models for different segments.

Credit scoring models are generally trained using customers credit history and demographics. Recent works in interdisciplinary studies have showed that alternative source of information, such as psychological traits or behavioural attitudes, can aid to improve default prediction. In our work, we would like to verify if an explorative segmentation based on financial knowledge, preferences and personality traits is able to detect different customer typologies also in terms of financial credit performance. This could be the case when the bank management has to deal with new clients or with those without a long banking history. The segmentation has been derived employing Hierarchical clustering on Factors coming from non-linear Principal Component Analysis (PCA). The Kernel-PCA allowed to map data indirectly in a very-high-dimensional space F where is simple to construct a hyperplane that divides the points into arbitrary clusters. The choice of the kernel functions and its parameters provided different kernel factors which produced, in turn, alternative clusters solutions. All the partitions have been ranked using alternative criteria that measure aspects of clusters validity.

In children with acute lung injury the endogenous surfactant system is altered via a variety of different mechanisms, including inflammation, vascular dysfunction, oxidant injury, cellular injury and oedema. This article examines the pathophysiology of acute lung injury and surfactant use for treatment of acute respiratory failure in infants and children.

L’analisi dell’evoluzione della soddisfazione nel contesto di business, è di solito basato su studi pseudo-panel, perchè sono meno costosi e facili da costruire con i dati disponibili. Come in casi cross-section, informazioni dettagliate sui clienti vengono raccolte in ogni istante temporale, ma il confronto dinamico avviene generalmente considerando soli pochi ritardi temporali (a causa del breve ciclo di vita di prodotti e servizi). Di conseguenza, nel nostro contributo proponiamo l’utilizzo dell’analisi fattoriale multipla duale. Tale tecnica permette la sintesi delle tabelle multivariate e la visualizzazione delle stesse su uno spazio comune che fa luce su traiettorie di soddisfazione dei clienti. I vantaggi della tecnica vengono illustrati in un caso di studio relativo ad una banca italiana The analysis of the evolution of satisfaction in business context is usually based on pseudo panels studies, because they are less costly and easy to build with the available data. As in the cross-section case, detailed information about customers are collected at each time point, but the dynamic comparison generally involves few temporal lags (due to short life time of products and services). Accordingly, in our paper we apply the Dual Multiple Factor Analysis. Such a technique allows the synthesis of the multivariate tables and their visualization on a common space that sheds light on customers’ trajectories of satisfaction. A real case study of an Italian bank is illustrated

In this paper we explore the relationship between the Industrial Production Index (IPI), the confidence index for the manufacturing sector and its sub-indexes and Google searches for several words linked to the economic situation, for the period January 2004 - September 2016 on Italian data. Significant correlations between the selected indicators point to probable comovements of same. Adding one observation at a time since the first forewarning signs of the 2008 crisis, we find that a few Google searches and the IPI cointegrate, particularly during the strong downward trend leading to January 2009, while no confidence indicators cointegrate with the IPI. These findings suggest that concern about economic conditions expressed through searches in google and the IPI or the confidence indexes are influenced by common circumstances. Recursive forecasts of the IPI through VECM models suggest that the evolution of the IPI can be well mimicked using the real time Gtrends selected variables.

We study frictions in trading patterns in the Euro money market. We characterize the structure of lending relations during the period of recent financial turmoil. We use a network-topology method on data from overnight transactions in the Electronic Market for Interbank Deposits (e-MID) to investigate two main issues. First, we characterize the roles of borrowers and lenders in long-run relationships by providing evidence on network formation at a 3-month frequency. Second, we identify the “key players” in the marketplace and study their behavior. In our formalization, key players are “locally-central banks” within a network that lend (or borrow) large volumes to (from) several counterparties, while borrowing (or lending) small volumes from (to) a small number of institutions. Our results are twofold. We show that the aggregate trading patterns in e-MID are characterized by largely asymmetric relations. This implies a clear difference in the roles of lenders and borrowers, with market positions changing only gradually over time. We also find that the large net lenders exploit their positions as network leaders by imposing aggressive pricing policies on their counterparties.

Background & Aims The model for end-stage liver disease (MELD) is used for organ allocation in liver transplantation (LT), but its prognostic performance is less accurate in patients with low score. We assess the outcome of patients with MELD 15.9. Conclusions In patients with low MELD (

Tail events, due to their nature, occur very rarely and are therefore often regarded as an element of surprise. However, business continuity management (BCM) as a part of enterprise risk management (ERM) should protect businesses from disruption in case of such events. In this chapter, we investigate whether businesses of different sizes were ready to withstand the consequences of the outbreak of the global SARS-CoV-2 pandemic in 2020. To this aim, we analyse the results of a global survey conducted during a major lockdown period (April–May 2020) by applying multiple correspondence analysis (MCA) and clustering. Our findings indicate that the financial sector was the best prepared for tail events, which may be attributed to regulations enforced for most financial institutions. Although around a half of businesses did not have a business continuity plan (BCP), having one helped adjust to the new conditions efficiently.

Kernel-based methods such as SVMs and LS-SVMs have been successfully used for solving various supervised classi cation and pattern recognition problems in machine learning. Unfortunately, they are heavily dependent on the choice of the optimal kernel function and from tuning parameters. Their solutions, in fact, su er of complete lack of interpretation in terms of input variables. That is not a banal problem, especially when the learning task is related with a critical asset of a business, like credit scoring, where deriving a classification rule has to respect an international regulation. The following strategy is proposed for solving problems using categorical predictors: replace the predictors by components issued from MCA, choice of the best kernel among several ones (linear ,RBF, Laplace, Cauchy, etc.), approximation of the classifi er through a linear model. The loss of performance due to such approximation is balanced by better interpretability for the end user, employed in order to understand and to rank the influence of each category of the variables set in the prediction. This strategy has been applied to real risk-credit data of small enterprises. Cauchy kernel was found the best and leads to a score much more e fficient than classical ones, even after approximation.

SARS-CoV-2 infection may impair behavior and mental health; we explored the psychological impact on parents and children who experienced COVID-19 within their families. A cross-sectional web-based survey was conducted on families attending the COVID-19 Follow-up Clinic at the Department for Women’s and Children’s Health, Padua (Italy). From April 2020 to August 2021, 75 surveys were collected from 66 families (97 parents and 129 children); almost 70% of participants had COVID-19, mostly asymptomatic/mildly symptomatic, and the median time from infection to survey compilation was 164.7 days (SD 56). Most parents (>87%) reported positive relationships with family members either before, during, or after COVID-19. More than one-third of children and adolescents were unable to adapt to isolation. Among 31 pre-school children with a median age of 3 (SD 1.7), a change of one or more functions was reported for 74.2% of cases irrespective of COVID-19 status, particularly a change in circadian rhythm (25%), in relationship with parents (42.8%), and poor emotional control (36%). Among 74 children with a median age of 10.9 years (SD 2.7), 8.1% had a score indicating a disease; however, significant impairment in attention was reported for 16.7%, along with anxiety/depression and problems with conduct in 5.6% and 6.5% of cases, respectively.

This paper addresses the issue of measuring tolerance, viewed as a multifaceted phenomenon involving several different social domains. We develop a multidimensional index for Likert-scale data, characterized by the following features: (i) it reflects the individual’s intensity of tolerant attitudes towards each social domain; (ii) the index can be broken down by dimension in order to determine the contribution of each dimension to overall tolerance; (iii) the index combines the different dimensions of tolerance using a weighted scheme that reflects the importance of each dimension in determining the overall level of tolerance. To show how this new measure of tolerance works in practice, we carry out a case study using an Italian recent survey asking the opinion of university students about different subjects, such as interreligious dialog, women/religion relationship, religion/death relationship, homosexuality, and multicultural society.

The containment measures following COVID-19 pandemic drastically reduced airway infections, but they also limited the access of patients to healthcare services. We aimed to assess the antibiotic prescription patterns in the Italian paediatric primary care setting before and after the containment measures implementation. For this retrospective analysis, we used a population database, Pedianet, collecting data of patients aged 0–14 years enrolled with family paediatricians (FP) from March 2019 to March 2021. Antibiotic prescriptions were classified according to WHO AWaRe classification. An interrupted time series evaluating the impact of the containment measures implementation on the monthly antibiotic index, on the access to watch index, and on the amoxicillin to co-amoxiclav index stratified by diagnosis was performed. Overall, 121,304 antibiotic prescriptions were retrieved from 134 FP, for a total of 162,260 children. From March 2020, the antibiotic index dropped by more than 80% for respiratory infections. The Access to Watch trend did not change after the containment measures, reflecting the propensity to prescribe more broad-spectrum antibiotics for respiratory infections even during the pandemic. Similarly, co-amoxiclav was prescribed more often than amoxicillin alone for all the diagnoses, with a significant variation in the trend slope for upper respiratory tract infections prescriptions.

Importance: Understanding the long-term immune response against SARS-CoV-2 infection in children is crucial to optimize vaccination strategies. Although it is known that SARS-CoV-2 antibodies may persist in adults 12 months after infection, data are limited in the pediatric population. Objective: To examine long-term anti-SARS-CoV-2 spike receptor-binding domain (S-RBD) IgG kinetics in children after SARS-CoV-2 infection. Design, Setting, and Participants: In this single-center, prospective cohort study, patients were enrolled consecutively from April 1, 2020, to August 31, 2021, at the COVID-19 Family Cluster Follow-up Clinic, Department of Women's and Children's Health, University Hospital of Padua. A cohort of 252 COVID-19 family clusters underwent serologic follow-up at 1 to 4, 5 to 10, and more than 10 months after infection with quantification of anti-S-RBD IgG by chemiluminescent immunoassay. Exposures: SARS-CoV-2 infection. Results: Among 902 study participants, 697 had confirmed SARS-CoV-2 infection, including 351 children or older siblings (mean [SD] age, 8.6 [5.1] years) and 346 parents (mean [SD] age, 42.5 [7.1] years). Among 697 cases, 674 (96.7%) were asymptomatic or mild. Children had significantly higher S-RBD IgG titers than older patients across all follow-up time points, with an overall median S-RBD IgG titer in patients younger than 3 years 5-fold higher than adults (304.8 [IQR, 139.0-516.6] kBAU/L vs 55.6 [24.2-136.0] kBAU/L, P

Small and medium enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. On the same time, SMEs are more fragile and likely to default as compared to larger firms so to induce public policies targeted on them. Specific default predic- tion models, accounting for SMEs idyiosincratic traits, are based on several types of data, mainly accounting indicators. We explore the possibility of complementing accounting information with data scraped from the firms’ websites.

The paper explores the possibility to employ the source code of corporate websites as an information source for research in innovation studies. Research in this area is generally based on studies that collect data on patents or official data sources. Our paper links the standard economic information of the firm with web-based data and joins the ongoing debate with a threefold contribution. First, whereas the majority of the literature focused on the linguistic content of web-pages, we mostly use HTML tags. Second, we propose a method to assess the quality of the linkage of Web data to firm-level information. Third, we show that the data retrieved from corporate websites can aid to identify 'innovative SMEs'.

[EN] Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies., This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033.

Differently from traditional transaction data, social media data are difficult to investigate due to their volume, variety and velocity. Indeed, the knowledge extraction from social media data raises several issues especially for what concerns statistical exploration and synthesis of complex information. Our work aims to study users' preferences, stated on a social media platform, in order to aid businesses to make their marketing communication decisions. We rely our analysis upon 5685 Italian Facebook users interested in pharmaceutical products and health. The data have been collected at the end of 2014 and are focused on Likes actively expressed by the subjects on specific categories of interests (TV Channels and Magazines). Through a factorial analysis we uncovered significant associations between marketing communication Media and users' profiles. This allows sketching out a marketing strategy in twofold actions: first, identifying the target group to reach and, then, the nearest suitable channel where to develop the marketing communication.

[EN] We propose the use of online indicators, scraped from the firms¿ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model., This work was partially supported by the Ca' Foscari University of Venice, Italy and by Agencia Estatal de Investigacion, Spain under grant PID2019107765RBI00. We also acknowledge helpful comments by an anonymous referee.

The kinin B1 receptor plays a critical role in the chronic phase of pain and inflammation. The development of B1 antagonists peaked in recent years but almost all promising molecules failed in clinical trials. Little is known about these molecules&rsquo, mechanisms of action and additional information will be necessary to exploit the potential of the B1 receptor. With the aim of contributing to the available knowledge of the pharmacology of B1 receptors, we designed and characterized a novel class of allosteric non-peptidic inhibitors with peculiar binding characteristics. Here, we report the binding mode analysis and pharmacological characterization of a new allosteric B1 antagonist, DFL20656. We analyzed the binding of DFL20656 by single point mutagenesis and radioligand binding assays and we further characterized its pharmacology in terms of IC50, B1 receptor internalization and in vivo activity in comparison with different known B1 antagonists. We highlighted how different binding modes of DFL20656 and a Merck compound (compound 14) within the same molecular pocket can affect the biological and pharmacological properties of B1 inhibitors. DFL20656, by its peculiar binding mode, involving tight interactions with N114, efficiently induced B1 receptor internalization and evoked a long-lasting effect in an in vivo model of neuropathic pain. The pharmacological characterization of different B1 antagonists highlighted the effects of their binding modes on activity, receptor occupancy and internalization. Our results suggest that part of the failure of most B1 inhibitors could be ascribed to a lack of knowledge about target function and engagement.

[EN] The Small Business Act of the European Commission in 2008 acknowledge s the key role of Small and Medium Enterprises (SMEs) in the EU economy. Th is is particularly relevant for Italy, which has the largest share of SMEs in Europe, as well as for other countries such as Portugal, Spain and Greece. On the other hand, SMEs experience more difficulties in their early stages mainly due to high market competition and credit constraints, as highlighted by Fritsch and Weyh (2006). For these reasons, the study of SMEs default risk is always relevant. There are several papers studying firm default factors in a single country (see Ciampi, 2015, Fantazzini and Figini, 2009, Flix and dos Santos, 2018). The literature concentrates mainly on financial indicators built on businesses’ balance sheets, which are available about two years late wi th respect to their reference period. This diminishes the significance of the results, both for credit risk and policy aims, and particularly in a forecasting perspective. The purpose of this paper is to provide a preliminary study on a sample of spanish firms selected from the SABI, Sistema de Análisis de Balances Ibéricos, which is listed among Bureau van Dijk databases. The analysis will be carried out according to both parametric and non-parametric discrimination techniques, with the standard construction of a training set on which to build a model and a validation set to test the validity and robustness of the results, and, in the end, the reliability of the model in predicting default. Finally we present a new proposal: a scheme to understand to what ext ent firms’ default can be predicted by substituting the traditional data sour ces (offline information) with data collected from their corporate websites (onli ne information) in order to exploit more up-to-date information.

The objective of quantitative credit scoring is to develop accurate models of classification. Most attention has been devoted to deliver new classifiers based on variables commonly used in the economic literature. Several interdisciplinary studies have found that personality traits are related to financial behaviour; therefore, psychological traits could be used to lower credit risk in scoring models. In our paper, we considered financial histories and psychological traits of customers of an Italian bank. We compared the performance of kernel-based classifiers with those of standard ones. We found very promising results in terms of misclassification error reduction when personality attitudes are included in models, with both linear and non-linear discriminants. We also measured the contribution of each variable to risk prediction in order to assess importance of each predictor.

Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.

The growth of Internet and the information technology has generated big changes in subjects' communication, which, nowadays, occurs through social media or via thematic forums. This challenges the traditional notion of Customer Relationship Management (CRM) and pushes businesses to prompt and accurate understanding of sentiments expressed, in order to address their marketing actions. In this paper, the authors propose a combined application of a supervised Sentiment Analysis (SA) with a probabilistic kernel discriminant to provide a robust classifier of texts polarization. The partition obtained is also described by means of a statistical characterization of the texts. Such an approach is very promising, not only in terms of classification accuracy, but also in terms of knowledge extraction. A real case study is illustrated in order to test and show the effectiveness of the proposed strategy.

Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this paper, we propose a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed; empirical results reveal a promising performance of the introduced strategy.

The objective of quantitative credit scoring is to develop accurate models that can distinguish between good and bad applicants (Baesens et al, 2003). Statistical research has focused on delivering new classification methodologies based on neural networks or support vector machines that provide better predictions respect to standard classifiers but poor interpretation. A step forward in such task, it is represented by linear reconstruction of kernel discriminant proposed by Liberati et al (2015) which combines the effective classification results, due to application of no-linear functions, with an easy interpretability of the data. In reality, insolvency, is not always a negative occurrence but the opportunity to generate more profit for a financial institution. Indeed, it can be considered a marketing leverage to reinforce customers’ loyalty. In this paper, we approach credit risk modeling into this perspective. We compare the classification solution that focuses on prediction performance with an explanatory regression as Tobit that models both default probability and duration. Results will be illustrated in a double perspective (risk management and customers' segmentation), in order to identify which covariates affect the insolvent behaviour the most.

[EN] The aim of this paper is to propose a method to explore and synthesize social media data in order to aid businesses to make their communication decisions. The research was conducted at the end of 2014 on 5607 Italian Facebook subjects interested in drugs and health. In this study, we refer to the pharmaceutical market that is characterized by strict legal constraints, which prevent any promotional activities (such as advertising) of companies on prescription drugs. Thus, pharmaceutical businesses tend to promote their corporate brand instead of a single product brand. In such context, social media offer the opportunity to gather customers’ information about their attitudes and preferences, helpful to address marketing activities. Through a multivariate statistical approach on Facebook data, we have highlighted the associations existing between TV channels and users’ profiles. Therefore, depending on the value proposition to promote, every business could choose, first, the target group to reach and, then, the nearest suitable channel where to develop the corporate brand communication.

Extant healthcare literature has demonstrated that providing oncological patients with detailed, continuous, and diversified information on their pathology positively affects patient empowerment and care continuum effectiveness. Medical information is traditionally conveyed by physicians and professional care givers. In the latest years, however, there has been a rapid rise in the use of web media as a source of health information. Improving cancer care continuum requires also the implementation of a reliable and high-quality oncological web communication system, as well as a deeper understanding of patients' requirements in this respect. To the best of our knowledge, however, no papers have investigated the importance and role of web communication, and of websites, in particular. This work aims to address these aspects explicitly, proposing the use of text mining as a tool for identifying oncological patient communication needs. For this purpose, we conducted an exploratory study on a sample of rare cancer patients. Text mining techniques were applied on transcriptions of semi-structured interviews on patients' information requirements, and the text content was synthetized. By means of correspondence analysis, the main concepts expressed by patients were identified, and the association between patients and concepts was represented in an appropriate metric space. Although our study is at an early stage, the findings highlighted by this preliminary text mining analysis could support the design of a patient-centered e-health communication system and be the basis for further analysis.

Customer satisfaction for banking services is, arguably, a construct that develops and changes over time for a number of different endogenous and exogenous factors (modification of customers contract terms, transparency of banking transactions and financial services, bank charges, customer relationships, changes of market conditions an so on). Measuring change requires a longitudinal perspective: it can be carried out collecting measurements on the same individuals across multiple time points. The aim of this paper is to analyze the dynamic customer satisfaction of particular sub-groups of clients estimating a Latent Growth Curve Model. Results show interesting behavior differences to address strategic management decisions.

The most important company asset seems to be Customer Satisfaction (CS), which banks, in the recent years, have frequently analyzed. For reaching such target, a dynamic Factor Analysis offers an effective way of merging information about clients and their preferences evolution. In our work we performed a dynamic Customer Satisfaction study, by means of a three-way factorial analysis, and we also introduced a new index of shift and shape (SSI), to synthesize information about every customer, cluster or typology. We considered a national bank case, with spread network, evaluating results provided by a questionnaire framed according to the SERVQUAL model. The information employed was obtained via a Customer Satisfaction survey repeated three times (waves). We performed the dynamic factorial model and we illustrated the usage of SSI as a new measure of trajectories’ dissimilarity. Finally, we showed our results which highlight promising performances of our index.

The growth of Internet and the information technology has generated big changes in subjects communication, that, nowadays, oc- curs through social media or via thematic forums. This produced a surge of information that is freely available: it offers the possibility to com- panies to evaluate their credibility and to monitor the ”mood” of their markets. The application of Sentiment Analysis (SA) has been proposed in order to extract, via objective rules, positive or negative opinions from (unstructured) texts. Communication literature, instead, highlights how such polarization derives from a subjective evaluations of the texts by the receivers. In business applications the receiver (i.e. marketing manager) is leaded by the values and the mission of the company. In our paper we propose a strategy to fit brand image and company values with a subjective SA, a probabilistic Kernel classifier has been employed to get discrimination rule and to rank classification results.