Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system
Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim...
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| Format: | Conference or Workshop Item |
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2013
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| _version_ | 1796859590332121088 |
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| author | Mohd Yunos, Zuriahati Shamsuddin, Siti Mariyam Ismail, Noriszura Sallehuddin, Roselina |
| author_facet | Mohd Yunos, Zuriahati Shamsuddin, Siti Mariyam Ismail, Noriszura Sallehuddin, Roselina |
| author_sort | Mohd Yunos, Zuriahati |
| collection | ePrints |
| description | Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity. |
| first_indexed | 2024-03-05T19:29:25Z |
| format | Conference or Workshop Item |
| id | utm.eprints-51176 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:29:25Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | utm.eprints-511762017-07-18T06:28:38Z http://eprints.utm.my/51176/ Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system Mohd Yunos, Zuriahati Shamsuddin, Siti Mariyam Ismail, Noriszura Sallehuddin, Roselina QA75 Electronic computers. Computer science Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity. 2013 Conference or Workshop Item PeerReviewed Mohd Yunos, Zuriahati and Shamsuddin, Siti Mariyam and Ismail, Noriszura and Sallehuddin, Roselina (2013) Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system. In: Proceedings Of The 20th National Symposium On Mathematical Sciences (SKSM20): Research In Mathematical Sciences: A Catalyst For Creativity And Innovation, PTS A And B. http://dx.doi.org/10.1063/1.4801297 |
| spellingShingle | QA75 Electronic computers. Computer science Mohd Yunos, Zuriahati Shamsuddin, Siti Mariyam Ismail, Noriszura Sallehuddin, Roselina Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title | Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title_full | Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title_fullStr | Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title_full_unstemmed | Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title_short | Modeling the Malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| title_sort | modeling the malaysian motor insurance claim using artificial neural network and adaptive neurofuzzy inference system |
| topic | QA75 Electronic computers. Computer science |
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