Career
Giudici began his career as an assistant professor of Statistics at the University of Pavia from 1994 to 1999. At the same institution,he also held other appointments,including associate professor of Statistics at the faculty of Economics from 2000 to 2006,full professor of Statistics at the faculty of Political Sciences from 2007 to 2011,and,since 2012,he has been a full professor of Statistics at the Department of Economics and Management. [1]
Giudici has coordinated European-funded research projects,including FIN-TECH and PERISCOPE. [2] He holds the appointment of specialty chief editor at the Artificial Intelligence in Finance (Frontiers). [3]
Research
To characterize the contagion impact between asset prices,Giudici introduced an extended Vector Autoregressive model based on network models,concluding that Bitcoin prices have a low correlation with traditional assets. [4] Together with Bussmann and others,he proposed an explainable AI model to analyze credit management and risk during peer-to-peer lending,assessing Shapley values,and analyzing 15,000 firms to identify non-risky and risky borrowers,as well as underscoring their financial characteristics. [5] He also conducted a study to evaluate two explainable AI models:Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive Explanations (SHAP),employing XGBoost to predict the default probability for Small and Medium Enterprises. The study highlighted that LIME has less discriminatory power than SHAP. [6]
Giudici authored a book titled Applied Data Mining:Statistical Methods for Business and Industry,in which he described applied data mining techniques (including computational and statistical modeling) within a unified statistical framework and demonstrated their practical applications. [7] Wei Jiang,in his review of the book,stated that "the book provides a detailed presentation of these case studies". However,he described the discussed criterion as "quite brief but transparent" and suggested that "it would be better to provide a clear link between different criteria". [8] Lesley F. Wright from Liverpool John Moores University commented that "the book's strength lies in the number and diversity of these case studies". [9] Richard J. Cleary characterized it as a book with many "nice features". However,he highlighted the presence of "some sections that are perhaps too difficult". [10]
Furthermore,Giudici and Raffinetti expanded the use of Lorenz Zonoids to develop assessment tools for the four main artificial intelligence trustworthiness criteria:S.A.F.E. (Sustainability,Accuracy,Fairness,and Explainability). [11]
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