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Research on UBI Auto Insurance Pricing Model Based on Adaptive SAPSO to Optimize the Fuzzy Controller

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Abstract

In order to accurately determine the auto insurance rate of UBI, this paper proposes to use fuzzy controller to calculate the rate and optimize it by using the simulated annealing particle swarm algorithm with Metropolis criterion. Firstly, a fuzzy controller is constructed by selecting monthly mileage and violation times to calculate the self-underwriting coefficient. In order to eliminate the subjectivity defect of fuzzy controller, the correlation function of independent underwriting coefficient and historical risk data is proposed as the fitness function of evaluating fuzzy rules, using adaptive simulated annealing particle swarm optimization algorithm is intelligent search, according to the fitness value of continual iteration and optimize the optimal fuzzy rules. Finally, the fuzzy controller is reconstructed with the optimal fuzzy rules to estimate the auto insurance rate accurately. The results show that the adaptive simulated annealing particle swarm optimization algorithm can effectively extract the driving behavior information and can calculate the more reasonable and accurate autonomous underwriting coefficient. The results are highly correlated with the number of historical accidents and have the ability and stability of risk quantification.

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References

  1. National Bureau of Statistics of People’s Republic of China, China Statistical Yearbook, (2018)

  2. Dai, M.: Business fare to enter the deep water area-sma and medium-sized insurance enterprises differentiation development for breakthrough, China Financial News Network (2018)

  3. Hu, X., Chiu, Y.-C., Ma, Y.-L., Zhu, L.: Studying driving risk factors using multi-source mobile computing data. Int. J. Transport. Sci. Technol. 4(3), 295–312 (2015)

    Article  Google Scholar 

  4. Dijksterhuis, C., Lewis-Evans, B., Jelijs, B., de Waard, D., Brookhuis, K., Tucha, O.: The impact of immediate or delayed feedback on driving behaviour in a simulated Pay-As-You-Drive system. Accid. Analy. Prev. 75, 93–104 (2015)

    Article  Google Scholar 

  5. Ma, Y.L., Zhu, X., Hu, X., Chiu, Y.C.: The use of context-sensitive insurance telematics data in auto insurance rate making. Transport. Res. 113, 243–258 (2018)

    Article  Google Scholar 

  6. Arbabzadeh, N., Jafari, M.A.: Data-driven approach for driving safety risk prediction using driver behavior and roadway information data. IEEE Trans. Intell. Transport. Syst. 99, 1–15 (2017)

    Google Scholar 

  7. Zhu, S.: UBI-based car insurance rate determination mode and method research. Beijing Jiaotong University, Beijing Jiaotong University (2015)

  8. Gao, Y.: UBI rate determination model based on driving behavior classification. Beijing Jiaotong University, Beijing (2017)

    Google Scholar 

  9. Liu, X., Feng, Y., Mi, H.: Application of GAMLSS model in auto insurance pricing. Math. Pract. Theory 47(11), 1–8 (2017)

    Google Scholar 

  10. Zhang, L., Wang, X.: Determination of auto insurance rates under large-value claims. Stat. Inform. Forum 34(01), 58–63 (2019)

    Google Scholar 

  11. Peng, J., Liu, N., Zhao, H.: Research on intelligent UBI system. Comput. Technol. Dev. 26(01), 142–146 (2016)

    Google Scholar 

  12. Wang, X.: Research on UBI pricing model based on natural driving data. Beijing Jiaotong University, Beijing (2018)

    Google Scholar 

  13. Garg, H.: An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol. Comput. 24, 1–10 (2015)

    Article  Google Scholar 

  14. Garg, H.: A hybrid GA-GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. In: Handbook of research on artificial intelligence techniques and algorithms. pp. 620–654, (2015)

  15. Garg, H.: A hybrid GSA-GA algorithm for constrained optimization problems. Inform. Sci. 478, 499–523 (2019)

    Article  Google Scholar 

  16. Garg, H.: A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274, 292–305 (2016)

    MathSciNet  MATH  Google Scholar 

  17. Patwal, R.S., Narang, N., Garg, H.: A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy 142, 822–837 (2018)

    Article  Google Scholar 

  18. Pan, A., Wang, L., Guo, W., Wu, Q.: A probability-based coevolving multi-objective algorithm for antenna array synthesis. Appl. Soft Comput. J. 73, 178–191 (2018)

    Article  Google Scholar 

  19. Meng, Q., Qiu, R., Zhang, M., et al.: Agricultural vehicle navigation system based on improved particle swarm optimization fuzzy control. Trans. Chin. Soc. Agric. Mach. 46(3), 29–36 (2015). 58

    Google Scholar 

  20. Wang, G., Huang, Z., Dai, M.: Research on fuzzy control of brushless motor based on improved particle swarm optimization algorithm. J. Guangxi Normal Univ. 34(2), 21–27 (2016)

    MATH  Google Scholar 

  21. Liu, Q.: Design and application of fuzzy controller based on particle swarm optimization, Shenyang University of Technology, Liaoning (2017)

  22. Eberhart, R., Kennedy, K.: A new optimizer using particle swarm theory, MHS’95. In: Proceedings of the sixth international symposium on micro machine and human science, pp. 39–43, (1995)

  23. Kirkpatrick, B.S., Gelatt, C., Vecchi, D.: Optimization by simulated annealing. Science 220, 71–80 (1983)

    Article  MathSciNet  Google Scholar 

  24. Jiang, L.: Internet car insurance UBI product design. Zhejiang University, Zhenjiang (2017)

    Google Scholar 

Download references

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Authors and Affiliations

  1. College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, China

    Chun Yan, Zhaochuang Ou, Wei Liu & Qiang Xu

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  1. Chun Yan
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  2. Zhaochuang Ou
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  3. Wei Liu
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  4. Qiang Xu
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Correspondence to Wei Liu.

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Yan, C., Ou, Z., Liu, W. et al. Research on UBI Auto Insurance Pricing Model Based on Adaptive SAPSO to Optimize the Fuzzy Controller. Int. J. Fuzzy Syst. 22, 491–503 (2020). https://doi.org/10.1007/s40815-019-00789-6

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  • DOI: https://doi.org/10.1007/s40815-019-00789-6

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