To equip students with the conceptual framework and analytical tools, including AI techniques, that will enable them to assess the creditworthiness of non-financial firms and produce their ratings, covering also emerging risk sources

To enable students to successfully apply for roles in banks, rating agencies, and private capital firms—specifically as Credit risk analysts, Quantitative analysts, and Risk model developers—and to strengthen their preparation towards Analyst positions in Investment banking and Corporate finance departments

The course has two parts. Part 1 deals with: (i) the standard credit analysis of firms by means of financial ratios and credit performance variables, employing multivariate logistic regression and yielding the statistical rating; (ii) the expert analysis of other information and risk sources, leading to the full rating of the firm. Part 2 deals with new data types and AI applications including: machine learning for credit risk assessment, sentiment analysis with natural language processing, climate risk, social and governance risk, cyber risk

Antonio Scalia (ed.), Corporate credit analysis and AI, 2026, Springer

Standard lectures, applied sessions involving R code, case studies. Experts and senior credit analysts will contribute to applied sessions and talks

Active classroom participation, group work, and final exam. Group work consists of credit risk assessment problems, including statistical analysis and estimation of the probability of default of a sample of firms by means of R. Final exam is largely based on the lecture notes. It has multiple choice questions and open-ended questions. Grade obtained in group work yields 1/3 of final grade. Final written exam yields 2/3 of final grade

Candidates should show a genuine interest and active participation throughout the course. They are normally expected to lead their group work and present the results. They are expected to achieve high or very high grades in the final exam.