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A Hierarchical Fuzzy Logic Control System for Malaysian Motor Tariff with Risk Factors

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Soft Computing in Data Science (SCDS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 652))

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Abstract

In many countries, including Malaysia, it is made compulsory to have a motor insurance policy and the premium is determined based on the Motor Tariff which ensures that a standard premium is imposed to the policyholders. At present, the premium in Malaysia includes only two factors which are the sum insured and the cubic capacity of the engine. Many existing methods used to calculate the tariff depend solely on the data and does not enable the experts to provide their input into the system. In contrast, the rule based system which is used in the Fuzzy Logic Control System could cater for the experts’ input. This research aims to develop a system that can determine the motor tariff using the Hierarchical Fuzzy Logic Control System. Besides the sum insured and the cubic capacity of the engine, the system will also incorporate the risk level of policyholders into the Motor Tariff. As a prototype, two selected risk factors are used, namely the age of drivers and the age of cars. The risk premium subsystem is developed before combining it with the main tariff premium system that constitute the Hierarchical Fuzzy Logic Control System. The result confirmed that the premium is loaded when the risk level is high and discounted when the risk level is low. The finding is in tandem with Bank Negara Malaysia (BNM) impending detariffication exercise for determining the motor insurance policy.

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References

  1. Mohd Yunos, Z., Shamsuddin, S., Ismail, N., Sallehuddin, R.: Modeling the Malaysian Motor Insurance Claim Using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System. In: 20th National Symposium on Mathematical Sciences, pp. 1431–1436. AIP Publishing, Kuala Lumpur (2013)

    Google Scholar 

  2. Baker, V., Kumar, S.: Motor premium rating. In: 5th Global Conference of Actuaries, pp. 52–58 (2003)

    Google Scholar 

  3. Dhesi, D.: De-tariffication of Motor and Fire Insurance Premiums Expected. Business News, Petaling Jaya (2015)

    Google Scholar 

  4. Sabhlok, R., Malattia, R.: Detariffication in the Malaysian general insurance sector. Towers Watson, Malaysia (2014)

    Google Scholar 

  5. Association of British Insurers (ABI): Insurance in the UK: The Benefits of Pricing Risk (2008)

    Google Scholar 

  6. Cheong, P., Jemain, A., Ismail, N.: Practice and pricing in non-life insurance: the malaysian experience. J. Qual. Meas. Anal., 11–24 (2008)

    Google Scholar 

  7. Bojadziev, G., Bojadziev, M.: Fuzzy Logic for Business, Finance and Management. World Scientific Publishing Co., Pte. Ltd., Singapore (2007)

    Book  MATH  Google Scholar 

  8. Wang, L.: A Course in Fuzzy Systems and Control. Prentice Hall Internationl Inc. (1996)

    Google Scholar 

  9. Kwang, H.: First Course of Fuzzy Theory and Applicaitions. Springer, Germany (2005)

    Google Scholar 

  10. Berhan, E., Abraham, A.: Hierarchical Fuzzy Logic System for Manuscript Evaluation. Middle-East J. Sci. Res. 19(9), 1235–1245 (2004)

    Google Scholar 

  11. Schouten, N., Salman, M., Kheir, N.: Fuzzy logic control system in hybrid vehicles. IEEE Trans. Contr. Syst. Technol. (2002)

    Google Scholar 

  12. Chuen, C.: Fuzzy logic in control systems: fuzzy logic controller, Part II. IEEE Trans. Syst., 419–433 (1990)

    Google Scholar 

  13. Fakhrahmad, S.M., Zare, A., Jahromi, M.Z.: Constructing accurate fuzzy rule-based classification systems using apriori principles and rule-weighting. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 547–556. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst., Man Cybern. 23(3), 665–685 (1993)

    Google Scholar 

  15. Chai, Y., Jia, L., Zhang, Z.: Mamdani model based adaptive neural fuzzy inference system and its application. Int. J. Comp. Intell. 5(1), 22–29 (2009)

    Google Scholar 

  16. Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzz. Syst. (2004)

    Google Scholar 

  17. Raju, G.V.S., Zhou, J., Kisner, R.A.: Hierarchical Fuzzy Control. Int. J. Control 54, 1201–1216 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  18. Renkas, K., Niewiadomski, A.: Hierarchical fuzzy logic systems: current research and perspectives. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 295–306. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  19. Mohammadian, M.: Designing customized hierarchical fuzzy logic system for modelling and prediction. In: 4th Asian Pacific Conference on Simulated Evolution and Learning, Singapore (2002)

    Google Scholar 

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Correspondence to Daud Mohamad .

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Mohamad, D., Jamal, L.D.M. (2016). A Hierarchical Fuzzy Logic Control System for Malaysian Motor Tariff with Risk Factors. In: Berry, M., Hj. Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2016. Communications in Computer and Information Science, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-2777-2_20

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  • DOI: https://doi.org/10.1007/978-981-10-2777-2_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2776-5

  • Online ISBN: 978-981-10-2777-2

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