Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company

Seyed Behnam Khakbaz; Nastaran Hajiheydari; Marziyeh Pourestarabadi

doi:doi:10.11648/j.ijber.20140303.12

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Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company

Seyed Behnam Khakbaz, Nastaran Hajiheydari, Marziyeh Pourestarabadi

Published in International Journal of Business and Economics Research (Volume 3, Issue 3)

Received: 27 April 2014 Accepted: 17 May 2014 Published: 30 May 2014

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Abstract

Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.

Published in	International Journal of Business and Economics Research (Volume 3, Issue 3)
DOI	10.11648/j.ijber.20140303.12
Page(s)	128-134
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), 2014. Published by Science Publishing Group

Keywords

Assessment, Insurance Industry, Car Insurance, Data Mining, Insurance Risk

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APA Style

Seyed Behnam Khakbaz, Nastaran Hajiheydari, Marziyeh Pourestarabadi. (2014). Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. International Journal of Business and Economics Research, 3(3), 128-134. https://doi.org/10.11648/j.ijber.20140303.12

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ACS Style

Seyed Behnam Khakbaz; Nastaran Hajiheydari; Marziyeh Pourestarabadi. Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. Int. J. Bus. Econ. Res. 2014, 3(3), 128-134. doi: 10.11648/j.ijber.20140303.12

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AMA Style

Seyed Behnam Khakbaz, Nastaran Hajiheydari, Marziyeh Pourestarabadi. Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. Int J Bus Econ Res. 2014;3(3):128-134. doi: 10.11648/j.ijber.20140303.12

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@article{10.11648/j.ijber.20140303.12,
  author = {Seyed Behnam Khakbaz and Nastaran Hajiheydari and Marziyeh Pourestarabadi},
  title = {Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company},
  journal = {International Journal of Business and Economics Research},
  volume = {3},
  number = {3},
  pages = {128-134},
  doi = {10.11648/j.ijber.20140303.12},
  url = {https://doi.org/10.11648/j.ijber.20140303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20140303.12},
  abstract = {Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.},
 year = {2014}
}

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TY - JOUR
T1 - Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company
AU - Seyed Behnam Khakbaz
AU - Nastaran Hajiheydari
AU - Marziyeh Pourestarabadi
Y1 - 2014/05/30
PY - 2014
N1 - https://doi.org/10.11648/j.ijber.20140303.12
DO - 10.11648/j.ijber.20140303.12
T2 - International Journal of Business and Economics Research
JF - International Journal of Business and Economics Research
JO - International Journal of Business and Economics Research
SP - 128
EP - 134
PB - Science Publishing Group
SN - 2328-756X
UR - https://doi.org/10.11648/j.ijber.20140303.12
AB - Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.
VL - 3
IS - 3
ER -

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Author Information

Seyed Behnam Khakbaz

Faculty of Management, University of Tehran, Tehran, Iran
Nastaran Hajiheydari

Faculty of Management, University of Tehran, Tehran, Iran
Marziyeh Pourestarabadi

Faculty of Management, University of Tehran, Tehran, Iran

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Plain Text BibTeX RIS

APA Style

Seyed Behnam Khakbaz, Nastaran Hajiheydari, Marziyeh Pourestarabadi. (2014). Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. International Journal of Business and Economics Research, 3(3), 128-134. https://doi.org/10.11648/j.ijber.20140303.12

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ACS Style

Seyed Behnam Khakbaz; Nastaran Hajiheydari; Marziyeh Pourestarabadi. Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. Int. J. Bus. Econ. Res. 2014, 3(3), 128-134. doi: 10.11648/j.ijber.20140303.12

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AMA Style

Seyed Behnam Khakbaz, Nastaran Hajiheydari, Marziyeh Pourestarabadi. Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company. Int J Bus Econ Res. 2014;3(3):128-134. doi: 10.11648/j.ijber.20140303.12

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@article{10.11648/j.ijber.20140303.12,
  author = {Seyed Behnam Khakbaz and Nastaran Hajiheydari and Marziyeh Pourestarabadi},
  title = {Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company},
  journal = {International Journal of Business and Economics Research},
  volume = {3},
  number = {3},
  pages = {128-134},
  doi = {10.11648/j.ijber.20140303.12},
  url = {https://doi.org/10.11648/j.ijber.20140303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20140303.12},
  abstract = {Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.},
 year = {2014}
}

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