This book presents cutting-edge methodologies, including AI techniques, for corporate credit analysis as developed for the in-house credit assessment system (ICAS) of Banca d’Italia. The first part illustrates the methods, applications and use cases of the existing system, that is employed to evaluate a large sample of Italian non-financial companies. The second part presents the rating systems in use at commercial banks and provides a benchmarking exercise of the latter systems against the ratings produced by ICAS. The third part presents new developments of the system, including the evaluation of climate transition risk, climate physical risk, social and governance factors, cyber risk, sentiment analysis. The book illustrates all state-of-the-art AI applications developed for ICAS, providing practical examples and empirical results."e;Scalia and colleagues at the Bank of Italy (BOI) provide a well written and important commentary report that shows that a  blend of classical, statistical models for corporate credit analysis combined with modern technological additions, such as AI, machine learning and ESG variables can promote value-added coverage and accuracy. We have also tested these credit enhancements using large language AI models, especially when analyzing global SME, privately owned firms -- so important to the health of most economies. Also, the blend of such techniques to the more opaque, growing private debt market can be a major analytical addition. BOI's extensive and unique databases and a talented group of analysts have made these enhancements possible."e;— Dr. Edward I. Altman, Professor Emeritus at the NYU Stern School of Business and Co-Founder of Wiserfunding, Ltd.

This book presents cutting-edge methodologies, including AI techniques, for corporate credit analysis as developed for the in-house credit assessment system (ICAS) of Banca d’Italia. The first part illustrates the methods, applications and use cases of the existing system, that is employed to evaluate a large sample of Italian non-financial companies. The second part presents the rating systems in use at commercial banks and provides a benchmarking exercise of the latter systems against the ratings produced by ICAS. The third part presents new developments of the system, including the evaluation of climate transition risk, climate physical risk, social and governance factors, cyber risk, sentiment analysis. The book illustrates all state-of-the-art AI applications developed for ICAS, providing practical examples and empirical results."e;Scalia and colleagues at the Bank of Italy (BOI) provide a well written and important commentary report that shows that a  blend of classical, statistical models for corporate credit analysis combined with modern technological additions, such as AI, machine learning and ESG variables can promote value-added coverage and accuracy. We have also tested these credit enhancements using large language AI models, especially when analyzing global SME, privately owned firms -- so important to the health of most economies. Also, the blend of such techniques to the more opaque, growing private debt market can be a major analytical addition. BOI's extensive and unique databases and a talented group of analysts have made these enhancements possible."e;— Dr. Edward I. Altman, Professor Emeritus at the NYU Stern School of Business and Co-Founder of Wiserfunding, Ltd.

This book presents cutting-edge methodologies, including AI techniques, for corporate credit analysis as developed for the in-house credit assessment system (ICAS) of Banca d’Italia. The first part illustrates the methods, applications and use cases of the existing system, that is employed to evaluate a large sample of Italian non-financial companies. The second part presents the rating systems in use at commercial banks and provides a benchmarking exercise of the latter systems against the ratings produced by ICAS. The third part presents new developments of the system, including the evaluation of climate transition risk, climate physical risk, social and governance factors, cyber risk, sentiment analysis. The book illustrates all state-of-the-art AI applications developed for ICAS, providing practical examples and empirical results."e;Scalia and colleagues at the Bank of Italy (BOI) provide a well written and important commentary report that shows that a  blend of classical, statistical models for corporate credit analysis combined with modern technological additions, such as AI, machine learning and ESG variables can promote value-added coverage and accuracy. We have also tested these credit enhancements using large language AI models, especially when analyzing global SME, privately owned firms -- so important to the health of most economies. Also, the blend of such techniques to the more opaque, growing private debt market can be a major analytical addition. BOI's extensive and unique databases and a talented group of analysts have made these enhancements possible."e;— Dr. Edward I. Altman, Professor Emeritus at the NYU Stern School of Business and Co-Founder of Wiserfunding, Ltd.