Predictive modelling for motor insurance claims using artificial neural networks

Zuriahati Mohd Yunos, Aida Ali, Siti Mariyam Shamsyuddin, Noriszura Ismail, Roselina Salleh Sallehuddin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The expected claim frequency and the expected claim severity are used in predictive modelling for motor insurance claims. There are two category of claims were considered, namely, third party property damage (TPPD) and own damage (OD). Data sets from the year 2001 to 2003 are used to develop the predictive model. The main issues in modelling the motor insurance claims are related to the nature of insurance data, such as huge information, uncertainty, imprecise and incomplete information; and classical statistical techniques which cannot handle the extreme value in the insurance data. This paper proposes the back propagation neural network (BPNN) model as a tool to model the problem. A detailed explanation of how the BPNN model solves the issues is provided.

Original languageEnglish
Pages (from-to)160-172
Number of pages13
JournalInternational Journal of Advances in Soft Computing and its Applications
Volume8
Issue number3
Publication statusPublished - 2016

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Insurance
Neural networks
Backpropagation

Keywords

  • Back propagation neural network
  • Claim frequency
  • Claim severity
  • Predictive modelling

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

Predictive modelling for motor insurance claims using artificial neural networks. / Yunos, Zuriahati Mohd; Ali, Aida; Shamsyuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina Salleh.

In: International Journal of Advances in Soft Computing and its Applications, Vol. 8, No. 3, 2016, p. 160-172.

Research output: Contribution to journalArticle

Yunos, Zuriahati Mohd ; Ali, Aida ; Shamsyuddin, Siti Mariyam ; Ismail, Noriszura ; Sallehuddin, Roselina Salleh. / Predictive modelling for motor insurance claims using artificial neural networks. In: International Journal of Advances in Soft Computing and its Applications. 2016 ; Vol. 8, No. 3. pp. 160-172.
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