Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company

James Akuma Bogonko; George Orwa; Anthony Wanjoya

doi:doi:10.11648/j.ajmcm.20200501.13

| Peer-Reviewed

Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company

James Akuma Bogonko, George Orwa, Anthony Wanjoya

Published in American Journal of Mathematical and Computer Modelling (Volume 5, Issue 1)

Received: 28 October 2019 Accepted: 21 November 2019 Published: 18 February 2020

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.

Published in	American Journal of Mathematical and Computer Modelling (Volume 5, Issue 1)
DOI	10.11648/j.ajmcm.20200501.13
Page(s)	18-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

AKI - Association of Kenya Insurers, Reserves, Product Limit Estimator, Cox Regression

References

[1]	Atkins, D. and Bates (2004). Risk, regulation and capital adequacy. The Chartered Insurance Institute Course book.
[2]	McClenahan, C. L. (2001). Ratemaking. Foundations of Causality Actuarial Science.
[3]	International, B. M. (2014). Kenya insurance report. Business Monitor International.
[4]	Brown, J. (1997). Insurance administration, life management institute Loma. Atlanta, Georgia
[5]	Wedge, P. and Handley, D. (2003). Claims management study. The Chartered Insurance Institute, Great Britain.
[6]	Kiebach, A. (2014). Five factors that affect car insurance rate. Lancaster Red Rose Credit Union.
[7]	AKI (2015). Motor insurance claims. Insurance guidebook, pages 1–66.
[8]	Czado, C. and Rudolph, F. (2002). Application of survival analysis methods to long term care insurance. Center of Mathematical Sciences, Munich University of Technology, Germany.
[9]	Ritesh, S. and Keshab, M. (2011). Survival analysis in clinical trials. National center for biotechnology information, U.S. National Library of Medicine 8600 Rockville Pike, Bethesda MD, 20894 USA.
[10]	Stevenson, M. (2009). An introduction to survival analysis. PhD thesis, Epicenter, IVABS, Massey University.
[11]	Wilesmith, J., Stevenson, M., King, C., & Morris, R. (2003). Spatio-temporal epidemiology of foot and mouth disease in two counties of Great Britain in 2001. Preventive Veterinary Medicine, 61 (3), 99- 170.
[12]	Tsiatis, A. and Zhang, D. (2005). Analysis of survival data. Statistics, North Carolina State University, Department of Statistics.
[13]	Dohoo, I., Martin, S., & Stryhn, H. (2003). Veterinary Epidemiologic Research. Charlottetown, Prince Edward Island, Canada: AVC Inc.
[14]	Kaplan, E. and Meier, P. (1958). Non-parametric estimation from incomplete observations. American statistical Association, 53, (282): 457-481.
[15]	Cox, D. and Oakes, D. (1984). Analysis of Survival Data: Monographs on Statistics and Applied Probability. London, New York. Chapman and Hall.

Cite This Article

Plain Text BibTeX RIS

APA Style

James Akuma Bogonko, George Orwa, Anthony Wanjoya. (2020). Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. American Journal of Mathematical and Computer Modelling, 5(1), 18-21. https://doi.org/10.11648/j.ajmcm.20200501.13

Copy | Download

ACS Style

James Akuma Bogonko; George Orwa; Anthony Wanjoya. Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. Am. J. Math. Comput. Model. 2020, 5(1), 18-21. doi: 10.11648/j.ajmcm.20200501.13

Copy | Download

AMA Style

James Akuma Bogonko, George Orwa, Anthony Wanjoya. Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. Am J Math Comput Model. 2020;5(1):18-21. doi: 10.11648/j.ajmcm.20200501.13

Copy | Download

@article{10.11648/j.ajmcm.20200501.13,
  author = {James Akuma Bogonko and George Orwa and Anthony Wanjoya},
  title = {Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {5},
  number = {1},
  pages = {18-21},
  doi = {10.11648/j.ajmcm.20200501.13},
  url = {https://doi.org/10.11648/j.ajmcm.20200501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20200501.13},
  abstract = {Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company
AU  - James Akuma Bogonko
AU  - George Orwa
AU  - Anthony Wanjoya
Y1  - 2020/02/18
PY  - 2020
N1  - https://doi.org/10.11648/j.ajmcm.20200501.13
DO  - 10.11648/j.ajmcm.20200501.13
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 18
EP  - 21
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20200501.13
AB  - Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.
VL  - 5
IS  - 1
ER  -

Copy | Download

Author Information

James Akuma Bogonko

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
George Orwa

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

James Akuma Bogonko, George Orwa, Anthony Wanjoya. (2020). Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. American Journal of Mathematical and Computer Modelling, 5(1), 18-21. https://doi.org/10.11648/j.ajmcm.20200501.13

Copy | Download

ACS Style

James Akuma Bogonko; George Orwa; Anthony Wanjoya. Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. Am. J. Math. Comput. Model. 2020, 5(1), 18-21. doi: 10.11648/j.ajmcm.20200501.13

Copy | Download

AMA Style

James Akuma Bogonko, George Orwa, Anthony Wanjoya. Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company. Am J Math Comput Model. 2020;5(1):18-21. doi: 10.11648/j.ajmcm.20200501.13

Copy | Download

@article{10.11648/j.ajmcm.20200501.13,
  author = {James Akuma Bogonko and George Orwa and Anthony Wanjoya},
  title = {Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {5},
  number = {1},
  pages = {18-21},
  doi = {10.11648/j.ajmcm.20200501.13},
  url = {https://doi.org/10.11648/j.ajmcm.20200501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20200501.13},
  abstract = {Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company
AU  - James Akuma Bogonko
AU  - George Orwa
AU  - Anthony Wanjoya
Y1  - 2020/02/18
PY  - 2020
N1  - https://doi.org/10.11648/j.ajmcm.20200501.13
DO  - 10.11648/j.ajmcm.20200501.13
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 18
EP  - 21
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20200501.13
AB  - Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.
VL  - 5
IS  - 1
ER  -

Copy | Download