The role of Explainable Artificial Intelligence in risk assessment: a study on the economic and epidemiologic impact

The growing application of black-box Artificial Intelligence algorithms in many real-world application is raising the importance of understanding how the models make their decision. The research field that aims to "open" the black-box and to make the predictions more interpretable, is referred as eXplainable Artificial Intelligence (XAI). Another important field of research, strictly related to XAI, is the compression of information, also referred as dimensionality reduction. Having a synthetic set of few variables that captures the behaviour and the relationships of many more variables can be an effective tool for XAI as well. Thus, the contribution of the present thesis is the development of new approaches in the field of explainability, working on the two complementary pillars of dimensionality reduction and variables importance. The convergence of the two pillars copes with the aim of helping decision makers with the interpretation of the results. This thesis is composed of seven chapters: an introduction and a conclusion plus five self contained sections reporting the corresponding papers. Chapter 1 proposes a PCA-based method to create a synthetic index to measure the condition of a country’s financial system, providing policy makers and financial institutions with a monitoring and policy tool that is easy to implement and update. In chapter 2, a Dynamic Factor Model is used to produce a synthetic index that is able to capture the time evolution of cross-country dependencies of financial variables. The index is proved to increase the accuracy in predicting the ease in accessing to financial funding. In chapter 3, a set of variables covering health, environmental safety infrastructures, demographic, economic and institutional effectiveness is used to test two methodologies to build an Epidemiological Susceptibility Risk index. The predictive power of both indexes is tested on forecasting task involving Macroeconomic variables. In chapter 4, the credit riskiness of Small Medium Enterprises (henceforth SMEs) is assessed by testing and assessing the increase of performance of a machine learning historical random forest model compared to an ordered probit model. The relevance of each variable in predicting SME credit risk is assessed by using Shapley values. In chapter 5, a dataset of Italian unlisted firms provides evidence of the importance of using market information when assessing the credit risk for SMEs. A non-linear dimensionality reduction technique is applied to assign market volatility from listed peers and to evaluate Merton's probability of default (PD). Results show the increase in accuracy of predicting the default of unlisted firms when using the evaluated PD. Moreover, the way PD affects the defaults is explored by assessing its contribution to the predicted outcome by the means of Shapley values.