Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

Zuriahati Mohd Yunos, Siti Mariyam Shamsuddin, Noriszura Ismail, Roselina Sallehuddin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
Pages1431-1437
Number of pages7
Volume1522
DOIs
Publication statusPublished - 2013
Event20th National Symposium on Mathematical Sciences - Research in Mathematical Sciences: A Catalyst for Creativity and Innovation, SKSM 2012 - Putrajaya
Duration: 18 Dec 201220 Dec 2012

Other

Other20th National Symposium on Mathematical Sciences - Research in Mathematical Sciences: A Catalyst for Creativity and Innovation, SKSM 2012
CityPutrajaya
Period18/12/1220/12/12

Fingerprint

inference
damage
performance prediction
preprocessing
forecasting
predictions

Keywords

  • ANFIS
  • ANN
  • Claim frequency
  • Claim severity
  • Motor insurance

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Mohd Yunos, Z., Shamsuddin, S. M., Ismail, N., & Sallehuddin, R. (2013). Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system. In AIP Conference Proceedings (Vol. 1522, pp. 1431-1437) https://doi.org/10.1063/1.4801297

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.

AIP Conference Proceedings. Vol. 1522 2013. p. 1431-1437.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mohd Yunos, Z, Shamsuddin, SM, Ismail, N & Sallehuddin, R 2013, Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system. in AIP Conference Proceedings. vol. 1522, pp. 1431-1437, 20th National Symposium on Mathematical Sciences - Research in Mathematical Sciences: A Catalyst for Creativity and Innovation, SKSM 2012, Putrajaya, 18/12/12. https://doi.org/10.1063/1.4801297
Mohd Yunos Z, Shamsuddin SM, Ismail N, Sallehuddin R. Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system. In AIP Conference Proceedings. Vol. 1522. 2013. p. 1431-1437 https://doi.org/10.1063/1.4801297
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. AIP Conference Proceedings. Vol. 1522 2013. pp. 1431-1437
@inproceedings{ae7b4033fd9b428ab949f77314963fd3,
title = "Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system",
abstract = "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.",
keywords = "ANFIS, ANN, Claim frequency, Claim severity, Motor insurance",
author = "{Mohd Yunos}, Zuriahati and Shamsuddin, {Siti Mariyam} and Noriszura Ismail and Roselina Sallehuddin",
year = "2013",
doi = "10.1063/1.4801297",
language = "English",
isbn = "9780735411500",
volume = "1522",
pages = "1431--1437",
booktitle = "AIP Conference Proceedings",

}

TY - GEN

T1 - Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

AU - Mohd Yunos, Zuriahati

AU - Shamsuddin, Siti Mariyam

AU - Ismail, Noriszura

AU - Sallehuddin, Roselina

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - ANFIS

KW - ANN

KW - Claim frequency

KW - Claim severity

KW - Motor insurance

UR - http://www.scopus.com/inward/record.url?scp=84876949802&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876949802&partnerID=8YFLogxK

U2 - 10.1063/1.4801297

DO - 10.1063/1.4801297

M3 - Conference contribution

SN - 9780735411500

VL - 1522

SP - 1431

EP - 1437

BT - AIP Conference Proceedings

ER -