Network based scoring models to improve credit risk management in peer to peer lending platforms

Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models

Monitoring Covid-19 contagion growth in Europe. CEPS Working Document No 2020/03, March 2020

We present an econometric model which can be employed to monitor the evolution of the COVID-19 contagion curve. The model is a Poisson autoregression of the daily new observed cases, and can dynamically show the evolution of contagion in different time periods and locations, allowing for the comparative evaluation of policy approaches. We present timely results for nine European countries currently hit by the virus. From the findings, we draw four main conclusions. First, countries experiencing an explosive process (currently France, Italy and Spain), combined with high persistence of contagion shocks (observed in most countries under investigation), require swift policy measures such as quarantine, diffuse testing and even complete lockdown. Second, in countries with high persistence but lower contagion growth (currently Germany) careful monitoring should be coupled with at least “mild” restrictions such as physical distancing or isolation of specific areas. Third, in some countries, such as Norway and Denmark, where trends seem to be relatively under control and depend on daily contingencies, with low persistence, the approach to restrictive measures should be more cautious since there is a risk that social costs outweigh the benefits. Fourth, countries with a limited set of preventive actions in place (such as the Netherlands, Switzerl

La nuova scheda per l’analisi sensoriale dell’aceto balsamico tradizionale

L’analisi sensoriale è uno strumento molto potente e utile nell’attribuzione qualitativa degli alimenti, essa è complementare alle analisi chimiche e strumentali perché porta informazioni di altra natura, complesse e non rilevabili in altro modo. Per questo motivo, i tentativi di “tarare” l’analisi sensoriale sulla composizione del balsamico sono infruttuosi e concettualmente scorretti. Un presupposto essenziale per assicurare l’efficacia dell’analisi sensoriale è che essa sia svolta con procedure che massimizzino l’indipendenza di giudizio degli assaggiatori sia all’interno del panel che nella sequenza dell’analisi del campione. In questo contesto gioca un ruolo chiave sia il metodo di assaggio che la scheda per la raccolta delle espressioni di gradimento

Building predictive models for feature selection in genomic mining

Building predictive models for genomic mining requires feature selection, as an essential preliminary step to reduce the large number of variable available. Feature selection is a process to select a subset of features which is the most essential for the intended tasks such as classification, clustering or regression analysis. In gene expression microarray data, being able to select a few genes not only makes data analysis efficient but also helps their biological interpretation. Microarray data has typically several thousands of genes (features) but only tens of samples. Problems which can occur due to the small sample size have not been addressed well in the literature. Our aim is to discuss some issues on feature selection in microarray data in order to select the most predictive genes. We compare classical approaches based on statistical tests with a new approach based on marker selection. Finally, we compare the best predictive model with a model derived from a boosting method

Explainable AI methods in cyber risk management

AbstractArtificial intelligence (AI) methods are becoming widespread, especially when data are not sufficient to build classical statistical models, as is the case for cyber risk management. However, when applied to regulated industries, such as energy, finance, and health, AI methods lack explainability. Authorities aimed at validating machine learning models in regulated fields will not consider black‐box models, unless they are supplemented with further methods that explain why certain predictions have been obtained, and which are the variables that mostly concur to such predictions. Recently, Shapley values have been introduced for this purpose: They are model agnostic, and powerful, but are not normalized and, therefore, cannot become a standardized procedure. In this paper, we provide an explainable AI model that embeds Shapley values with a statistical normalization, based on Lorenz Zonoids, particularly suited for ordinal measurement variables that can be obtained to assess cyber risk

Big data analysis for financial risk management

A very important area of financial risk management is systemic risk modelling, which concerns the estimation of the interrelationships between financial institutions, with the aim of establishing which of them are more central and, therefore, more contagious/subject to contagion. The aim of this paper is to develop a novel systemic risk model. A model that, differently from existing ones, employs not only the information contained in financial market prices, but also big data coming from financial tweets. From a methodological viewpoint, the novelty of our paper is the estimation of systemic risk models using two different data sources: financial markets and financial tweets, and a proposal to combine them, using a Bayesian approach. From an applied viewpoint, we present the first systemic risk model based on big data, and show that such a model can shed further light on the interrelationships between financial institutions

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Significance and management of acetic acid bacteria culture collections

Acetic acid bacteria (AAB) are obligate aerobic microorganisms which have large significance in human life. Traditionally, AAB species have been used to produce fermented food and beverages thanks to their ability to oxidize ethanol to acetic acid. Moreover, in the last decades, they have been extensively investigated for other industrial biotechnology applications as the development of processes for highvalue products or biosensors. The potential exploitation of AAB diversity requires the existence of microbial culture collections, which are able to supply not only strains but essential data for fundamental microbial research. Therefore, microbial collections can be helpful to provide critical insights into AAB physiology and metabolism as well as integrate sequence data with transcriptional and functional studies to better define complex traits and develop new potential microbial processes. This article reviews the significance of microbial collections, with an overview of the well-known European Biological Resources Centers (BRCs) collecting AAB, and provides an insight into their cultivability and metabolic activity. It also discusses appropriate techniques in preserving authentic strains, quality control implications, databases and BRC networking as well as connections among collections and stakeholders

I campioni del Palio Matildico e gli indicatori di qualità dell'aceto balsamico tradizionale

La valorizzazione e la tutela dell’Aceto Balsamico Tradizionale, al pari di qualsiasi altro prodotto alimentare tradizionale, non può prescindere dalla conoscenza delle ragioni che lo rendono così specifico e particolare. La ricerca scientifica, oltre ad essere lo strumento di conoscenza necessario alla comprensione della complessità dell’ABT, è anche veicolo di promozione, nel rispetto della chiarezza, dell’autenticità e dell’oggettività, senza ricorrere a spiegazioni fantasiose, che nulla hanno a che fare con la concretezza dell’ABT, frutto dell’acquisizione empirica della conoscenza