Hostname: page-component-5c6d5d7d68-wbk2r Total loading time: 0 Render date: 2024-08-10T05:42:26.290Z Has data issue: false hasContentIssue false
  • English
  • Français

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

Published online by Cambridge University Press:  27 December 2021

Shengwang Meng ,
He Wang ,
Yanlin Shi  and
Guangyuan Gao
Shengwang Meng
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872, E-Mail: mengshw@ruc.edu.cn
He Wang
Affiliation:
Department of Finance, Shenzhen Humanities & Social Sciences Key Research Bases and Ying Shang Nan Ke Actuarial Science Center, Southern University of Science and Techology, Shenzhen, China, 518055, E-Mail: wangh9@sustc.edu.cn
Yanlin Shi
Affiliation:
Department of Actuarial Studies and Business Analytics, Macquarie University, North Ryde2109, Australia, E-Mail: yanlin.shi@mq.edu.au
Guangyuan Gao*
Affiliation:
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China, 100872 E-Mail: guangyuan.gao@ruc.edu.cn
*
E-Mail: guangyuan.gao@ruc.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Keywords

Telematics car driving dataautomobile insurance pricingone-dimensional convolutional neural networklimited fluctuation credibility modelgeneralized linear modelclaims frequencyG22C45
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 2 , May 2022 , pp. 363 - 391
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ayuso, M., Guillen, M. and Pérez-MarÍn, A.M. (2016a) Telematics and gender discrimination: Some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks, 4(2), 10.10.3390/risks4020010CrossRefGoogle Scholar
Ayuso, M., Guillen, M. and Pérez-MarÍn, A.M. (2016b) Using GPS data to analyse the distance traveled to the first accident at fault in pay-as-you-drive insurance. Transportation Research Part C: Emerging Technologies, 68, 160–167.CrossRefGoogle Scholar
Boucher, J.-P., CÔté, S. and Guillen, M. (2017) Exposure as duration and distance in telematics motor insurance using generalized additive models. Risks, 5(4): 54.10.3390/risks5040054CrossRefGoogle Scholar
Carvalho, E., Ferreira, B.V., Ferreira, J., de Souza, C., Carvalho, H.V., Suhara, Y., Pentland, A.S. and Pessin, G. (2017) Exploiting the use of recurrent neural networks for driver behavior profiling. In 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3016–3021.10.1109/IJCNN.2017.7966230CrossRefGoogle Scholar
Cather, D. A. (2020) Reconsidering insurance discrimination and adverse selection in an era of data analytics. Geneva Papers on Risk and Insurance - Issues and Practice, 45, 426–456.CrossRefGoogle Scholar
Cevolini, A. and Esposito, E. (2020) From pool to profile: Social consequences of algorithmic prediction in insurance. Big Data & Society, 7(2).CrossRefGoogle Scholar
Delong, L., Lindholm, M. and Wüthrich, M.V. (2021) Making Tweedie’s compound Poisson model more accessible. European Actuarial Journal, 11, 185–226.CrossRefGoogle Scholar
Denuit, M., Guillen, M. and Trufin, J. (2019) Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data. Annals of Actuarial Science, 13(2), 378–399.10.1017/S1748499518000349CrossRefGoogle Scholar
Gao, G., Shi, Y. and Wang, H. (2021) Telematics car driving data analytics. The Society of Actuaries Research Institute, pp. 1–50.Google Scholar
Gao, G., Wang, H. and Wüthrich, M.V. (2021) Boosting poisson regression models with telematics car driving data. Machine Learning.10.2139/ssrn.3596034CrossRefGoogle Scholar
Geyer, A., Kremslehner, D. and Muermann, A. (2020) Asymmetric information in automobile insurance: Evidence from driving behavior. Journal of Risk and Insurance, 87(4), 969–995.CrossRefGoogle Scholar
Girma, A., Yan, X. and Homaifar, A. (2019) Driver identification based on vehicle telematics data using LSTM recurrent neural network. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 894–902.CrossRefGoogle Scholar
Guillen, M., Nielsen, J.P., Pérez-MarÍn, A.M. and Elpidorou, V. (2020) Can automobile insurance telematics predict the risk of near-miss events? North American Actuarial Journal, 24(1), 141–152.10.1080/10920277.2019.1627221CrossRefGoogle Scholar
Ho, S.-H., Wong, Y.D. and Chang, V.W.C. (2014) Developing Singapore driving cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmospheric Environment, 97, 353–362.10.1016/j.atmosenv.2014.08.042CrossRefGoogle Scholar
Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer feedforward networks are universal approximators, Neural Networks, 2(5), 359–366.CrossRefGoogle Scholar
Hu, X., Zhu, X., Ma, Y.-L., Chiu, Y.-C. and Tang, Q. (2019) Advancing usage-based insurance – A contextual driving risk modelling and analysis approach. IET Intelligent Transport Systems, 13(3), 453–460.10.1049/iet-its.2018.5194CrossRefGoogle Scholar
Huang, Y. and Meng, S. (2019). Automobile insurance classification ratemaking based on telematics driving data, Decision Support Systems, 127.CrossRefGoogle Scholar
Hung, W. T., Tong, H. Y., Lee, C. P., Ha, K. and Pao, L. Y. (2007). Development of practical driving cycle construction methodology: A case study in Hong Kong. Transportation Research Part D: Transport and Environment, 12(2), 115–128.CrossRefGoogle Scholar
Joubert, J.W., de Beer, D. and de Koker, N. (2016) Combining accelerometer data and contextual variables to evaluate the risk of driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 4, 80–96.CrossRefGoogle Scholar
Kamble, S.H., Mathew, T.V. and Sharma, G.K. (2009) Development of real-world driving cycle: Case study of Pune, India. Transportation Research Part D: Transport and Environment, 14(2), 132–140.CrossRefGoogle Scholar
LeCun, Y., Bengio, Y. and Hinton, G. (n.d.) Deep learning. Nature, 521, 436–444.CrossRefGoogle Scholar
Lemaire, J., Park, S. C. and Wang, K. (2016) The use of annual mileage as a rating variable. ASTIN Bulletin, 46(1), 39–69.CrossRefGoogle Scholar
Ma, Y.-L., Zhu, X., Hu, X. and Chiu, Y.-C. (2018) The use of context-sensitive insurance telematics data in auto insurance rate making. Transportation Research Part A: Policy and Practice, 113, 243–258.Google Scholar
Mahapatra, A. (2019) Privacy: A growing risk in the insurance industry. Creative Components, 410.Google Scholar
Masood, S., Rai, A., Aggarwal, A., Doja, M.N. and Ahmad, M. (2020) Detecting distraction of drivers using convolutional neural network. Pattern Recognition Letters, 139, 79–85.CrossRefGoogle Scholar
Paefgen, J., Staake, T. and Fleisch, E. (2014) Multivariate exposure modeling of accident risk: Insights from pay-as-you-drive insurance data. Transportation Research Part A: Policy and Practice, 61, 27–40.Google Scholar
Richman, R. (2020) AI in actuarial science - A review of recent advances. Annals of Actuarial Science.10.1017/S174849952000024XCrossRefGoogle Scholar
Savelonas, M., Mantzekis, D., Labiris, N., Tsakiri, A., Karkanis, S. and Spyrou, E. (2020) Hybrid time-series representation for the classification of driving behaviour. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA), pp. 1–6.10.1109/SMAP49528.2020.9248460CrossRefGoogle Scholar
Singh, D. (2016) Using convolutional neural networks to perform classification on state farm insurance driver images, Technical report, Stanford University.Google Scholar
Verbelen, R., Antonio, K. and Claeskens, G. (2018) Unraveling the predictive power of telematics data in car insurance pricing. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67(5), 1275–1304.Google Scholar
Wahlström, J., Skog, I. and Händel, P. (2015) Detection of dangerous cornering in GNSS-data-driven insurance telematics. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3073–3083.CrossRefGoogle Scholar
Wahlström, J., Skog, I. and Händel, P. (2017) Smartphone-based vehicle telematics: A ten-year anniversary. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2802–2825.10.1109/TITS.2017.2680468CrossRefGoogle Scholar
Wahlström, J., Skog, I., Nordström, R.L. and Händel, P. (2018) Fusion of OBD and GNSS measurements of speed. IEEE Transactions on Instrumentation and Measurement 67(7), 1659–1667.CrossRefGoogle Scholar
Weidner, W., Transchel, F.W.G. and Weidner, R. (2016) Classification of scale-sensitive telematic observables for riskindividual pricing. European Actuarial Journal, 6(1), 3–24.CrossRefGoogle Scholar
Weidner, W., Transchel, F.W.G. and Weidner, R. (2017) Telematic driving profile classification in car insurance pricing. Annals of Actuarial Science, 11(2), 213–236.CrossRefGoogle Scholar
Wüthrich, M. V. (2021) The balance property in neural network modelling. Statistical Theory and Related Fields, 1–9.Google Scholar
Yang, Y., Qian, W. and Zou, H. (2016) Insurance premium prediction via gradient tree-boosted Tweedie compound Poisson models. Journal of Business and Economic Statistics, 36(3), 456–470.CrossRefGoogle Scholar
Supplementary material: File

Meng et al. supplementary material

Meng et al. supplementary material

Download Meng et al. supplementary material(File)
File 3.2 MB