Alessandro Bitetto

Biographical Note

Alessandro Bitetto is an Assistant Professor (RTT) of Economic Statistics at the School of Economics and Management of LIUC – Università Cattaneo. He was previously an Assistant Professor (RUTD A) of Statistics at the University of Pavia. He has carried out research and development activities in industry as a Senior Data Scientist at McKinsey & Company, Senior Quantitative Developer at Ernst & Young, and PhD intern at Neosperience, and is also co-founder and Head of R&D of the fintech spin-off 110 Laude, specialized in credit risk services for SMEs. He obtained his PhD in Computer Science from the University of Pavia in 2022 with a thesis on Explainable Artificial Intelligence for economic and epidemiological risk assessment.

He takes part in national and international research projects, including the Horizon 2020 PERISCOPE project on the socio-economic impacts of the COVID-19 pandemic and the PRIN PNRR project “Climate risk and uncertainty: environmental sustainability and asset pricing,” focused on the relationship between climate risk and financial returns. His work has been published, among others, in the Journal of Corporate Finance, Review of Quantitative Finance and Accounting, Socio-Economic Planning Sciences, Finance Research Letters, International Review of Financial Analysis and Scientific Reports. He has carried out research visits at Singapore Management University and is active in international conferences on statistics, quantitative finance, and data science.

Research interests

Credit Risk & Financial Stability

- ML models for SME default prediction

- Financial soundness and macroprudential analysis

Composite Indicators & Data Integration

- Data-driven environmental and epidemiological indices

- Robust weighting, PCA/factor methods, index validation

Explainable Machine Learning

- XAI methods for risk assessment and policy-relevant models

Fintech, ESG & Crypto Analytics

- ESG text analysis

- ICO performance, underpricing, and disclosure metrics

Advanced Statistical Learning

- Dynamic factor models and mixed-frequency forecasting