Machine learning techniques for mortality modeling

Philippe Deprez; Pavel V. Shevchenko; Mario V. Wüthrich

doi:10.2139/ssrn.2921841

Machine learning techniques for mortality modeling

Philippe Deprez^*, Pavel V. Shevchenko, Mario V. Wüthrich

^*Corresponding author for this work

Department of Applied Finance

Research output: Contribution to journal › Article

6 Citations (Scopus)

Abstract

Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling.

Original language	English
Pages (from-to)	337-352
Number of pages	16
Journal	European Actuarial Journal
Volume	7
Issue number	2
DOIs	https://doi.org/10.2139/ssrn.2921841 https://doi.org/10.1007/s13385-017-0152-4
Publication status	Published - 1 Dec 2017

Keywords

mortality modeling
cause-of-death mortality
machine learning
boosting
regression

Access to Document

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Machine learning techniques for mortality modeling'. Together they form a unique fingerprint.

Cite this

Deprez, P., Shevchenko, P. V., & Wüthrich, M. V. (2017). Machine learning techniques for mortality modeling. European Actuarial Journal, 7(2), 337-352. https://doi.org/10.2139/ssrn.2921841, https://doi.org/10.1007/s13385-017-0152-4

@article{ae4e85da981b4308a216979bb758c75a,

title = "Machine learning techniques for mortality modeling",

abstract = "Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling.",

keywords = "mortality modeling, cause-of-death mortality, machine learning, boosting, regression",

author = "Philippe Deprez and Shevchenko, {Pavel V.} and W{\"u}thrich, {Mario V.}",

year = "2017",

month = "12",

day = "1",

doi = "10.2139/ssrn.2921841",

language = "English",

volume = "7",

pages = "337--352",

journal = "European Actuarial Journal",

issn = "2190-9733",

publisher = "Springer, Springer Nature",

number = "2",

}

TY - JOUR

T1 - Machine learning techniques for mortality modeling

AU - Deprez, Philippe

AU - Shevchenko, Pavel V.

AU - Wüthrich, Mario V.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling.

AB - Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of death in mortality modeling.

KW - mortality modeling

KW - cause-of-death mortality

KW - machine learning

KW - boosting

KW - regression

UR - http://www.scopus.com/inward/record.url?scp=85036551972&partnerID=8YFLogxK

U2 - 10.2139/ssrn.2921841

DO - 10.2139/ssrn.2921841

M3 - Article

VL - 7

SP - 337

EP - 352

JO - European Actuarial Journal

JF - European Actuarial Journal

SN - 2190-9733

IS - 2

ER -