Peer-reviewed articles

• Pittarello G., Hiabu M. and Villegas A. (2022), Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle. North American Actuarial Journal (accepted) ArXiV:2301.03858.

• Bladt, M. and Pittarello G. (2024) Individual claims reserving using the Aalen–Johansen estimator. ASTIN Bullettin - The Journal of the IAA. Published online 2024:1-21. doi:10.1017/asb.2024.28. ArXiV:2311.07384

• Pittarello G., Luini E. and Marchione MM. GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science. Published online 2024:1-37. doi:10.1017/S1748499524000022. ArXiV:2303.01129.

Submitted manuscripts

• Hiabu M., Hofman E. D., and Pittarello G. (2023), A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. ArXiV:2312.14549

Technical reports

• Hiabu, Munir, Emil Hofman, and Gabriele Pittarello. 2025. “Claim Counts Prediction Using Individual Data with ReSurv.” CAS E-Forum Quarter 1 (April).

• McGuire, G., Pittarello, G. (2022). Reserving with glms in Python. Machine Learning in Reserving Working Party, Institute and Faculty of Actuaries (IFoA).

Software

• Hofman E. D., Pittarello G., and Hiabu M. (2023), ReSurv (R package).

• Pittarello G., Hiabu M., and Villegas A. (2022), clmplus (R package).

• Pittarello G., Luini E. and Marchione M.M. (2022), GEMAct (Python package).