Settembre 2024
Matteo Lizzi
Centro di Ricerche Assicurative “Ermanno Pitacco”
Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with this problem usually use a two-population approach, modeling a mortality spread between a larger reference population and the population of interest, via likelihood based techniques.
To broaden the tools at actuaries’ disposal to build small-population mortality tables, a general structure for a two-step two-population model is proposed, its main element of novelty residing in a machine-learning-based approach to mortality spread estimation.
In order to obtain this, Contrast Trees and the related Estimation Contrast Boosting techniques have been applied.
A quite general machine-learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from the Human Mortality Database.
Finally, results from the ML-based model have been compared to those obtained from the traditional model.
Lo studio è pubblicato in Risk - Life Insurance and Pensions: Latest Advances and Prospects
DOWNLOAD ARTICOLO COMPLETO
To broaden the tools at actuaries’ disposal to build small-population mortality tables, a general structure for a two-step two-population model is proposed, its main element of novelty residing in a machine-learning-based approach to mortality spread estimation.
In order to obtain this, Contrast Trees and the related Estimation Contrast Boosting techniques have been applied.
A quite general machine-learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from the Human Mortality Database.
Finally, results from the ML-based model have been compared to those obtained from the traditional model.
Lo studio è pubblicato in Risk - Life Insurance and Pensions: Latest Advances and Prospects
DOWNLOAD ARTICOLO COMPLETO
Matteo Lizzi
Matteo Lizzi è membro del Core Faculty di MIB Trieste School of Management, nell'area Scienze Attuariali. È ricercatore affiliato al Centro di Ricerche Assicurative "Ermanno Pitacco".
Lizzi è membro qualificato dell'O.N.A. – l'Ordine Nazionale degli Attuari. Con otto anni di esperienza nel settore assicurativo, principalmente come attuario vita, ha conseguito un dottorato in Scienze Attuariali presso l'Università Sapienza di Roma, con la tesi "Improving Mortality Diagnostics and Estimation through Contrast Trees".
I suoi interessi di ricerca si focalizzano sull'applicazione di tecniche di Machine Learning alle scienze attuariali.
