Búsqueda

Mortality forecasting via multi-task neural networks

Portada
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20260001777
003  MAP
005  20260202104854.0
008  260130e20250512bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
24500‎$a‎Mortality forecasting via multi-task neural networks‎$c‎Luca De Mori...[et al.]
520  ‎$a‎Understanding and projecting mortality dynamics has become increasingly critical for pension systems and life insurers. While traditional stochastic and deterministic models have long been used to extrapolate mortality patterns beyond observed data, recent advances in machine learning offer new modelling opportunities. In particular, neural networks (NNs) provide strong computational capabilities and the flexibility to capture complex relationships without relying on explicit probabilistic assumptions. In this study, they develop a multi-task neural network framework that jointly learns from multiple related populations, enabling shared information to enhance forecasting accuracy across countries. Using mortality data from 17 nations, they evaluate the performance of these multi-task NNs against both single-task NN architectures and established stochastic mortality models. Their findings highlight the potential of multi-task learning to improve long-term mortality projections and support actuarial applications.
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20080599300‎$a‎Tablas de mortalidad
650 4‎$0‎MAPA20080624842‎$a‎Redes neuronales artificiales
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20090033023‎$a‎Estadística matemática
650 4‎$0‎MAPA20130014791‎$a‎Proyecciones
650 4‎$0‎MAPA20080603120‎$a‎Procesos estocásticos
7001 ‎$0‎MAPA20260001388‎$a‎Mori, Luca De
7102 ‎$0‎MAPA20100017661‎$a‎International Actuarial Association
7730 ‎$w‎MAP20077000420‎$g‎12/05/2025 Volume 55 Issue 2 - may 2025 , p. 313 - 331‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association