September 2024
Matteo Lizzi
Centre for Insurance Research “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.
The study is published in Risk - Life Insurance and Pensions: Latest Advances and Prospects
DOWNLOAD FULL ARTICLE
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.
The study is published in Risk - Life Insurance and Pensions: Latest Advances and Prospects
DOWNLOAD FULL ARTICLE
Matteo Lizzi - Core Faculty
Matteo Lizzi is a member of the Core Faculty at MIB Trieste School of Management, in the Actuarial Sciences area. He is a researcher affiliated with the Center for Insurance Research "Ermanno Pitacco".
Lizzi is a fully qualified member of O.N.A. – the Italian National Order of Actuaries. With eight years of experience in the insurance industry, primarily as a life actuary, he also holds a Ph.D. in Actuarial Sciences from Sapienza University of Rome, where his thesis focused on "Improving Mortality Diagnostics and Estimation through Contrast Trees."
His research interests center on applying machine learning techniques to actuarial sciences.
