Society and Economy
Volume/Issue:
Volume 41: Issue 3
Applications of Metaheuristics in Insurance
Author:
László Kovács Corvinus University of Budapest, Hungary

Search for other papers by László Kovács in
Current site
Google Scholar
PubMed Close
Pages:
371–395
Online Publication Date:
Sep 2019
Publication Date:
01 Sep 2019
Article Category:
Research Article
DOI:
https://doi.org/10.1556/204.2019.008
Keywords:
metaheuristics; Cox regression; insurance; model selection; regularization methods; best subsets; G22; C34; C52; C63; C65
Open access

When calculating different profitability measures for a life insurance company, one of the most important parameters to know is the probability of a policy being in force at any given time after the start of risk bearing. These probabilities are given by the survival function. In this paper, we examine data from a Hungarian insurance company, in order to build models for the survival functions of two life insurance products. For survival function estimation based on the unique parameters of a new policy, Cox regression is used. However, not all parameters of a new policy are relevant in estimating the survival function. Therefore, application of model selection algorithms is needed. Furthermore, if the exact effects of the policy parameters for the survival function can be determined, the insurance company can direct its sales team to acquire policies with positive technical results. When traditional model selection techniques proposed by the literature (such as best subset, stepwise and regularization methods) are applied on our data, we find that the effect of the selected predictors for survival cannot be determined, as there is a harmful degree of multicollinearity. In order to tackle this problem, we propose adding the hybrid metaheuristic from Láng et al. (2017) to the Cox regression in order to eliminate multicollinearity from the final model. On the test sets, performance of the models from the metaheuristic rivals those of the traditional algorithms with the use of noticeably less predictors. These predictors are not significantly correlated and are significant for survival, as well. It is shown in the paper that with the application of metaheuristics, we could produce a model with good predicting capabilities and interpretable predictor effects. These predictor effects can be used to direct the sales activities of the insurance company.

  • Allen, G. I. (2013): Automatic Feature Selection via Weighted Kernels and Regularization. Journal of Computational and Graphical Statistics 22(2): 284–299.

    • Search Google Scholar
    • Export Citation

  • Arnold, T. B. (2017): KerasR: R Interface to the Keras Deep Learning Library. The Journal of Open Source Software 2.

  • Begg, C. B. – Cramer, L. D. – Venkatraman, E. S. – Rosai, J. (2000): Comparing Tumour Staging and Grading Systems: A Case Study and a Review of the Issues, Using Thymoma as a Model. Statistics in Medicine 19(15): 1997–2014.

    • Search Google Scholar
    • Export Citation

  • Breheny, P. (2013): ncvreg: Regularization Paths for SCAD-and MCP-Penalized Regression Models. R package version 2.6-0.

  • Calaway, R. – Weston, S. (2014): doParallel: Foreach Parallel Adaptor for the Parallel Package. R package version 1 (8).

  • Calcagno, V. – de Mazancourt, C. (2010): glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models. Journal of Statistical Software 34(12): 1–29.

    • Search Google Scholar
    • Export Citation

  • Cox, D. (1972): Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological) 34(2): 187–220.

    • Search Google Scholar
    • Export Citation

  • Cox, D. R. (1975): Partial Likelihood. Biometrika: 269–276.

  • Fan, J. – Li, R. (2002): Variable Selection for Cox's Proportional Hazards Model and Frailty Model. Annals of Statistics 74–99.

  • Fan, J. – Feng, Y. – Wu, Y. (2010): High-Dimensional Variable Selection for Cox's Proportional Hazards Model. In: Berger, J. O. – Cai, T. T. – Johnstone, I. M. (eds): Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence D. Brown. Beachwood, OH: Institute of Mathematical Statistics, pp. 70–86.

    • Search Google Scholar
    • Export Citation

  • Furnival, G. M. – Wilson, R. W. (1974): Regressions by Leaps and Bounds. Technometrics 16(4): 499–511.

  • Gillespie, B. (2006): Checking Assumptions in the Cox Proportional Hazards Regression Model. Presented at the Midwest SAS Users Group (MWSUG) Dearborn, Michigan.

    • Search Google Scholar
    • Export Citation

  • Grosen, A. – Jørgensen, P. L. (2000): Fair Valuation of Life Insurance Liabilities: The Impact of Interest Rate Guarantees, Surrender Options, and Bonus Policies. Insurance: Mathematics and Economics 26(1): 37–57.

    • Search Google Scholar
    • Export Citation

  • Jia, J. – Yu, B. (2010): On Model Selection Consistency of the Elastic Net. Statistica Sinica 20: 595–611.

  • Láng, B. – Kovács, L. (2014): Linear Regression Model Selection Using Improved Harmony Search Algorithm. SEFBIS Journal 9(1): 15–22.

    • Search Google Scholar
    • Export Citation

  • Láng, B. – Kovács, L. – Mohácsi, L. (2017): Linear Regression Model Selection Using a Hybrid Genetic – Improved Harmony Search Parallelized Algorithm. SEFBIS Journal 11(1): 2–9.

    • Search Google Scholar
    • Export Citation

  • Leng, C. – Zhang, H. (2006): Model Selection in Nonparametric Hazard Regression. Nonparametric Statistics 18(7–8): 417–429.

  • Lumley, T. – Therneau, T. (2004): The Survival Package. R News 4(1): 26–28.

  • Minerva, T. – Paterlini, S. (2010): Regression Model Selection Using Genetic Algorithms. Proceedings of the 11th WSEAS International Conference on RECENT Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing: 19–27.

    • Search Google Scholar
    • Export Citation

  • Saldana, D. F. – Feng, Y. (2018): SIS: An R Package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models. Journal of Statistical Software 83(2): 1–25.

    • Search Google Scholar
    • Export Citation

  • Sheldon, T. J. – Smith, A. D. (2004): Market Consistent Valuation of Life Assurance Business. British Actuarial Journal 10(3): 543–605.

    • Search Google Scholar
    • Export Citation

  • Tibshirani, R. (1997): The Lasso Method for Variable Selection in the Cox Model. Statistics in Medicine 16(4): 385–395.

  • Vanderhoof, I. T. – Altman, E. (Eds.) (2013): The Fair Value of Insurance Liabilities (Vol. 1). Springer Science & Business Media.

  • Zhang, Z. (2016): Variable Selection with Stepwise and Best Subset Approaches. Annals of Translational Medicine 4(7): 136.

  • Zhao, P. – Yu, B. (2006): On Model Selection Consistency of Lasso. The Journal of Machine Learning Research 7: 2541–2563.

  • Collapse
  • Expand
Cover Society and Economy
Society and Economy
Print ISSN:
1588-9726
Online ISSN:
1588-970X

Editor-in-chief: Balázs SZENT-IVÁNYI

Co-Editors:

  • Péter MARTON (Corvinus University, Budapest)
  • István KÓNYA (Corvinus University, Budapest)
  • László SAJTOS (The University of Auckland)
  • Gábor VIRÁG (University of Toronto)

Associate Editors:

  • Tamás BOKOR (Corvinus University, Budapest)
  • Sándor BOZÓKI (Corvinus University Budapest)
  • Bronwyn HOWELL (Victoria University of Wellington)
  • Hintea CALIN (Babeş-Bolyai University)
  • Christian EWERHART (University of Zürich)
  • Clemens PUPPE (Karlsruhe Institute of Technology)
  • Zsolt DARVAS (Bruegel)
  • Szabina FODOR (Corvinus University Budapest)
  • Sándor GALLAI (Corvinus University Budapest)
  • László GULÁCSI (Óbuda University)
  • Dóra GYŐRFFY (Corvinus University Budapest)
  • György HAJNAL (Corvinus University Budapest)
  • Krisztina KOLOS (Corvinus University Budapest)
  • Alexandra KÖVES (Corvinus University Budapest)
  • Lacina LUBOR (Mendel University in Brno)
  • Péter MEDVEGYEV (Corvinus University Budapest)
  • Miroslava RAJČÁNIOVÁ (Slovak University of Agriculture)
  • Ariel MITEV (Corvinus University Budapest)
  • Éva PERPÉK (Corvinus University Budapest)
  • Petrus H. POTGIETER (University of South Africa)
  • Sergei IZMALKOV (MIT Economics)
  • Anita SZŰCS (Corvinus University Budapest)
  • László TRAUTMANN (Corvinus University Budapest)
  • Trenton G. SMITH (University of Otago)
  • György WALTER (Corvinus University Budapest)
  • Zoltán CSEDŐ (Corvinus University Budapest)
  • Zoltán LŐRINCZI (Ministry of Human Capacities)

Society and Economy
Institute: Corvinus University of Budapest
Address: Fővám tér 8. H-1093 Budapest, Hungary
Phone: (36 1) 482 5406
E-mail: balazs.szentivanyi@uni-corvinus.hu

Indexing and Abstracting Services:

  • CABELLS Journalytics
  • DOAJ
  • International Bibliographies IBZ and IBR
  • International Political Science Abstracts
  • JSTOR
  • SCOPUS
  • RePEc
  • Referativnyi Zhurnal

 

2023  
Scopus  
CiteScore 1.5
CiteScore rank Q2 (Sociology and Political Science)
SNIP 0.496
Scimago  
SJR index 0.243
SJR Q rank Q3

Society and Economy
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 900 EUR/article with enough waivers
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Sufficient number of full waiver available. Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

Society and Economy
Language English
Size B5
Year of
Foundation		 1972
Volumes
per Year		 1
Issues
per Year		 4
Founder Budapesti Corvinus Egyetem
Founder's
Address		 H-1093 Budapest, Hungary Fővám tér 8.
Publisher Akadémiai Kiadó
Publisher's
Address		 H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher		 Chief Executive Officer, Akadémiai Kiadó
ISSN 1588-9726 (Print)
ISSN 1588-970X (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Apr 2024 0 41 3
May 2024 0 15 5
Jun 2024 0 24 2
Jul 2024 0 53 4
Aug 2024 0 36 9
Sep 2024 0 37 2
Oct 2024 0 9 1