Universiti Teknologi Malaysia Institutional Repository

Hybrid predictive modelling for motor insurance claim

Mohd. Yunos, Z. and Shamsuddin, S. M. and Sallehuddin, R. and Alwee, R. (2019) Hybrid predictive modelling for motor insurance claim. In: International Conference on Green Engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019, 4-5 Feb 2019, Eastin Hotel Makkasan Bangkok, Thailand.

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Official URL: https://dx.doi.org/10.1088/1757-899X/551/1/012075


The objective of this study is to propose a new hybrid model to predict the Malaysia motor insurance claim by estimating the two important components; claim frequency and claim severity. The proposed model are integrating between grey relational analysis and back propagation neural network. We proposed the hybrid model to handle the issue of the insurance data and the complexity of classical statistical technique. Moreover, the classic statistical techniques are incapable of handling huge information in the insurance data. Thus, hybrid model is proposed because it has a high learning ability and capability to learn. Finally, a comparative analysis is carried out to evaluate the predictive model performance between GRABPNN and BPNN. The results produce by MAPE show a small percentage and thus, show that GRABPNN model provides more accurate predictive results compared to BPNN model.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:backpropagation, green computing, neural networks
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:89741
Deposited By: Narimah Nawil
Deposited On:22 Feb 2021 09:44
Last Modified:22 Feb 2021 09:44

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