Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.

Longevity risk management through Machine Learning: state of the art / Levantesi, Susanna; Nigri, Andrea; Piscopo, Gabriella. - In: INSURANCE MARKETS AND COMPANIES. - ISSN 2616-3551. - 1:11(2020), pp. 11-20. [10.21511/ins.11(1).2020.02]

Longevity risk management through Machine Learning: state of the art

Susanna Levantesi;Andrea Nigri
Primo
;
2020

Abstract

Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.
2020
classification; regression tree; longevity risk; machine learning; neural network
01 Pubblicazione su rivista::01a Articolo in rivista
Longevity risk management through Machine Learning: state of the art / Levantesi, Susanna; Nigri, Andrea; Piscopo, Gabriella. - In: INSURANCE MARKETS AND COMPANIES. - ISSN 2616-3551. - 1:11(2020), pp. 11-20. [10.21511/ins.11(1).2020.02]
File allegati a questo prodotto
File Dimensione Formato  
Levantesi_Longevity-risk-management_2020.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 339.95 kB
Formato Adobe PDF
339.95 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1466346
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? ND
social impact