• Zuriahati Mohd Yunos Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Siti Mariyam Shamsuddin Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Razana Alwee Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Noriszura Ismail Pusat Pengajian Sains Matematik, Universiti Kebangsaan Malaysia, Selangor, Malaysia
  • Roselina Salleh@Sallehuddin Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia



Predictive modelling, claim frequency, claim severity, regression, BPNN, ANFIS


The expected claim frequency and the expected claim severity are used in predictive modelling motor insurance claims. There are two categories of claims were considered, namely, third party property damage and own damage. Datasets from the year 2001 to 2003 are used to develop the predictive model. This paper proposes three different methods, namely, regression analysis, back propagation neural network and adaptive neuro fuzzy inference system to model claim frequency and claim severity as the two important elements in modelling the motor insurance claims. The experimental results showed that the back propagation neural network model produces more accurate as compared to regression analysis and adaptive neuro fuzzy inference system in predicting the claim frequency and claim severity. For both OD and TPPD claim, the results have shown the lowest MAPE with 0.2191 and 0.6515, and 0.2169 and 0.326, respectively.


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