A key point to assess the application of statistical learning models in Artificial Intelligence (AI) is the evaluation of their predictive accuracy. This because the "automatic" choice of an action crucially depends on the made prediction. While the best model in terms of fit to the observed...

A trustworthy application of Artificial Intelligence requires to measure in advance its possible risks. When applied to regulated industries, such as banking, finance and insurance, Artificial Intelligence methods lack explainability and, therefore, authorities aimed at monitoring risks may not...

In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity...

Lending to Small and Medium Enterprises (SME) is facilitated by the availability of advanced Machine Learning (ML) methods, embedded in financial technologies, which can accurately predict financial performance from the many data sources available. However, despite their high predictive...

A key point in the application of data science models is the evaluation of their accuracy. Statistics and machine learning have provided, over the years, a number of summary measures aimed at measuring the accuracy of a model in terms of its predictions, such as the Area under the ROC curve and...

We propose a general methodology framework for eXplainable credit scoring to provide interpretability of each individual variable and measure fairness. Specifically, it is able to detect important variables and quantifies their individual impact on a firm’s credit classification via the...

Financial technologies, boosted by the availability of machine learning models, are expanding in all areas of finance: from payments (peer to peer lending) to asset management (robot advisors) to payments (blockchain coins). Machine learning models typically achieve a high accuracy at the...

We proposes a two-layered tree network model that decomposes financial contagion into a global component, composed of inter-country contagion effects, and a local component, made up of inter-institutional contagion channels. The model is effectively applied to a database containing time series...

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of...