{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T07:39:14Z","timestamp":1776411554160,"version":"3.51.2"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71901207"],"award-info":[{"award-number":["71901207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003006","name":"ETH Zurich","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the emergence of telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with telematics car driving information. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.<\/jats:p>","DOI":"10.1007\/s10994-021-05957-0","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T17:02:40Z","timestamp":1616346160000},"page":"243-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Boosting Poisson regression models with telematics car driving data"],"prefix":"10.1007","volume":"111","author":[{"given":"Guangyuan","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario V.","family":"W\u00fcthrich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,21]]},"reference":[{"issue":"3","key":"5957_CR1","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1017\/asb.2020.27","volume":"50","author":"KC \u00c1goston","year":"2020","unstructured":"\u00c1goston, K. C., & Gyetvai, M. (2020). Joint optimization of transition rules and the premium scale in a bonus-malus system. ASTIN Bulletin, 50(3), 743\u2013776.","journal-title":"ASTIN Bulletin"},{"key":"5957_CR2","doi-asserted-by":"crossref","unstructured":"Ayuso, M., Guill\u00e9n, M., & P\u00e9rez-Mar\u00edn, A. M. (2016a). Telematics and gender discrimination: Some usage-based evidence on whether men\u2019s risk of accidents differs from women\u2019s. Risks 4\/2, article 10.","DOI":"10.3390\/risks4020010"},{"key":"5957_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.trc.2016.04.004","volume":"68","author":"M Ayuso","year":"2016","unstructured":"Ayuso, M., Guill\u00e9n, M., & P\u00e9rez-Mar\u00edn, 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\u2013167.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"5957_CR4","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/s11116-018-9890-7","volume":"46","author":"M Ayuso","year":"2019","unstructured":"Ayuso, M., Guill\u00e9n, M., & Nielsen, J. P. (2019). Improving automobile insurance ratemaking using telematics: Incorporating mileage and driver behaviour data. Transportation, 46, 735\u2013752.","journal-title":"Transportation"},{"key":"5957_CR5","doi-asserted-by":"crossref","unstructured":"Boucher, J.-P., C\u00f4t\u00e9, S., & Guill\u00e9n, M. (2017). Exposure as duration and distance in telematics motor insurance using generalized additive models. Risks 5\/4, article 54.","DOI":"10.3390\/risks5040054"},{"issue":"3","key":"5957_CR6","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1017\/asb.2014.11","volume":"44","author":"J-P Boucher","year":"2014","unstructured":"Boucher, J.-P., & Inoussa, R. (2014). A posteriori ratemaking with panel data. ASTIN Bulletin, 44(3), 587\u2013612.","journal-title":"ASTIN Bulletin"},{"key":"5957_CR7","unstructured":"Boucher, J.-P., & Pigeon, M. (2018). A claim score for dynamic claim counts modeling. arXiv https:\/\/arxiv.org\/abs\/1812.06157."},{"issue":"4","key":"5957_CR8","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1046\/j.0022-4367.2003.00066.x","volume":"70","author":"N Brouhns","year":"2003","unstructured":"Brouhns, N., Guill\u00e9n, M., Denuit, M., & Pinquet, J. (2003). Bonus-malus scales in segmented tariffs with stochastic migration between segments. The Journal of Risk and Insurance, 70(4), 577\u2013599.","journal-title":"The Journal of Risk and Insurance"},{"key":"5957_CR9","unstructured":"Chollet, F., & Allaire, J. J. (2018). Deep Learning with R. Manning Publication."},{"issue":"1","key":"5957_CR10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1017\/S0515036100006358","volume":"10","author":"N De Pril","year":"1978","unstructured":"De Pril, N. (1978). The efficiency of a bonus-malus system. ASTIN Bulletin, 10(1), 59\u201372.","journal-title":"ASTIN Bulletin"},{"issue":"2","key":"5957_CR11","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1017\/S1748499518000349","volume":"13","author":"M Denuit","year":"2019","unstructured":"Denuit, M., Guill\u00e9n, M., & Trufin, J. (2019). Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data. Annals of Actuarial Science, 13(2), 378\u2013399.","journal-title":"Annals of Actuarial Science"},{"key":"5957_CR12","doi-asserted-by":"publisher","DOI":"10.1002\/9780470517420","volume-title":"Actuarial Modelling of Claim Counts: Risk Classification. Credibility and Bonus-Malus Systems","author":"M Denuit","year":"2007","unstructured":"Denuit, M., Mar\u00e9chal, X., Pitrebois, S., & Walhin, J.-F. (2007). Actuarial Modelling of Claim Counts: Risk Classification. Credibility and Bonus-Malus Systems. Wiley."},{"key":"5957_CR13","doi-asserted-by":"crossref","unstructured":"Ferrario, A., Noll, A., & W\u00fcthrich, M. V. (2018). Insights from inside neural networks. SSRN, Abstract Id: 3226852.","DOI":"10.2139\/ssrn.3226852"},{"issue":"2","key":"5957_CR14","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1080\/03461238.2018.1523068","volume":"2019","author":"G Gao","year":"2019","unstructured":"Gao, G., Meng, S., & W\u00fcthrich, M. V. (2019). Claims frequency modeling using telematics car driving data. Scandinavian Actuarial Journal, 2019(2), 143\u2013162.","journal-title":"Scandinavian Actuarial Journal"},{"key":"5957_CR15","doi-asserted-by":"crossref","unstructured":"Gao, G., & W\u00fcthrich, M. V. (2019). Convolutional neural network classification of telematics car driving data. Risks 7\/1, article 6.","DOI":"10.3390\/risks7010006"},{"key":"5957_CR16","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press."},{"issue":"8","key":"5957_CR17","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1080\/03461238.2018.1429300","volume":"2018","author":"R Henckaerts","year":"2018","unstructured":"Henckaerts, R., Antonio, K., Clijsters, M., & Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018(8), 681\u2013705.","journal-title":"Scandinavian Actuarial Journal"},{"key":"5957_CR18","unstructured":"Henckaerts, R., C\u00f4t\u00e9, M.-P., Antonio, K., & Verbelen, R. (2019). Boosting insights in insurance tariff plans with tree-based machine learning. arXiv, 1904.10890."},{"key":"5957_CR19","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.atmosenv.2014.08.042","volume":"97","author":"S-H Ho","year":"2014","unstructured":"Ho, S.-H., Wong, Y.-D., & Chang, V.W.-C. (2014). Developing Singapore driving cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmospheric Environment, 97, 353\u2013362.","journal-title":"Atmospheric Environment"},{"key":"5957_CR20","doi-asserted-by":"crossref","unstructured":"Huang, Y., & Meng, S. (2019). Automobile insurance classification ratemaking based on telematics driving data. Decision Support Systems 127, article 113156.","DOI":"10.1016\/j.dss.2019.113156"},{"issue":"2","key":"5957_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.trd.2007.01.002","volume":"12","author":"WT Hung","year":"2007","unstructured":"Hung, W. T., Tong, H. Y., Lee, C. P., Ha, K., & 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\u2013128.","journal-title":"Transportation Research Part D: Transport and Environment"},{"issue":"2","key":"5957_CR22","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.trd.2008.11.008","volume":"14","author":"SH Kamble","year":"2009","unstructured":"Kamble, S. H., Mathew, T. V., & 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\u2013140.","journal-title":"Transportation Research Part D: Transport and Environment"},{"key":"5957_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-0631-3","volume-title":"Bonus-Malus Systems in Automobile Insurance","author":"J Lemaire","year":"1995","unstructured":"Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publisher."},{"issue":"1","key":"5957_CR24","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1017\/asb.2015.25","volume":"46","author":"J Lemaire","year":"2016","unstructured":"Lemaire, J., Park, S. C., & Wang, K. (2016). The use of annual mileage as a rating variable. ASTIN Bulletin, 46(1), 39\u201369.","journal-title":"ASTIN Bulletin"},{"issue":"1","key":"5957_CR25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1017\/asb.2020.40","volume":"51","author":"SCK Lee","year":"2021","unstructured":"Lee, S. C. K. (2021). Addressing imbalanced insurance data through zero-inflated Poisson regression with boosting. ASTIN Bulletin, 51(1), 27\u201355.","journal-title":"ASTIN Bulletin"},{"issue":"3","key":"5957_CR26","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1017\/S0515036100011028","volume":"6","author":"K Loimaranta","year":"1972","unstructured":"Loimaranta, K. (1972). Some asymptotic properties of bonus systems. ASTIN Bulletin, 6(3), 233\u2013245.","journal-title":"ASTIN Bulletin"},{"key":"5957_CR27","first-page":"27","volume":"61","author":"J Paefgen","year":"2014","unstructured":"Paefgen, J., Staake, T., & 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\u201340.","journal-title":"Transportation Research Part A: Policy and Practice"},{"key":"5957_CR28","doi-asserted-by":"publisher","DOI":"10.1017\/S1748499520000238","author":"R Richman","year":"2020","unstructured":"Richman, R. (2020a). AI in actuarial science \u2013 a review of recent advances \u2013 part 1. Annals of Actuarial Science. https:\/\/doi.org\/10.1017\/S1748499520000238.","journal-title":"Annals of Actuarial Science"},{"key":"5957_CR29","doi-asserted-by":"publisher","DOI":"10.1017\/S174849952000024X","author":"R Richman","year":"2020","unstructured":"Richman, R. (2020b). AI in actuarial science \u2013 a review of recent advances \u2013 part 2. Annals of Actuarial Science. https:\/\/doi.org\/10.1017\/S174849952000024X.","journal-title":"Annals of Actuarial Science"},{"key":"5957_CR30","doi-asserted-by":"crossref","unstructured":"Sun, S., Bi, J., Guill\u00e9n, M., & P\u00e9rez-Mar\u00edn, A.M. (2020). Assessing driving risk using internet of vehicles data: an analysis based on generalized linear models. Sensors 20\/9, article 2712.","DOI":"10.3390\/s20092712"},{"key":"5957_CR31","first-page":"1275","volume":"67","author":"R Verbelen","year":"2018","unstructured":"Verbelen, R., Antonio, K., & 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, 1275\u20131304.","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"issue":"1","key":"5957_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/asb.2020.34","volume":"51","author":"RM Verschuren","year":"2021","unstructured":"Verschuren, R. M. (2021). Predictive claim scores for dynamic multi-product risk classification in insurance. ASTIN Bulletin, 51(1), 1\u201325.","journal-title":"ASTIN Bulletin"},{"issue":"1","key":"5957_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s13385-016-0127-x","volume":"6","author":"W Weidner","year":"2016","unstructured":"Weidner, W., Transchel, F. W. G., & Weidner, R. (2016). Classification of scale-sensitive telematic observables for riskindividual pricing. European Actuarial Journal, 6(1), 3\u201324.","journal-title":"European Actuarial Journal"},{"issue":"2","key":"5957_CR34","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1017\/S1748499516000130","volume":"11","author":"W Weidner","year":"2017","unstructured":"Weidner, W., Transchel, F. W. G., & Weidner, R. (2017). Telematic driving profile classification in car insurance pricing. Annals of Actuarial Science, 11(2), 213\u2013236.","journal-title":"Annals of Actuarial Science"},{"issue":"3","key":"5957_CR35","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/TIT.2017.2776228","volume":"64","author":"T Wiatowski","year":"2018","unstructured":"Wiatowski, T., & B\u00f6lcskei, H. (2018). A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Transactions on Information Theory, 64(3), 1845\u20131866.","journal-title":"IEEE Transactions on Information Theory"},{"issue":"1","key":"5957_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/asb.2018.42","volume":"49","author":"MV W\u00fcthrich","year":"2019","unstructured":"W\u00fcthrich, M. V., & Merz, M. (2019). Editorial: Yes, we CANN! ASTIN Bulletin, 49(1), 1\u20133.","journal-title":"ASTIN Bulletin"},{"issue":"3","key":"5957_CR37","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1080\/07350015.2016.1200981","volume":"36","author":"Y Yang","year":"2018","unstructured":"Yang, Y., Qian, W., & Zou, H. (2018). Insurance premium prediction via gradient tree-boosted Tweedie compound Poisson models. Journal of Business and Economic Statistics, 36(3), 456\u2013470.","journal-title":"Journal of Business and Economic Statistics"},{"issue":"32","key":"5957_CR38","doi-asserted-by":"publisher","first-page":"4790","DOI":"10.1364\/AO.29.004790","volume":"29","author":"W Zhang","year":"1990","unstructured":"Zhang, W., Itoh, K., Tanida, J., & Ichioka, Y. (1990). Parallel distributed processing model with local space-invariant interconnections and its optical architecture. Applied Optics, 29(32), 4790\u20134797.","journal-title":"Applied Optics"},{"key":"5957_CR39","unstructured":"Zhang, W., Tanida, J., Itoh, K., & Ichioka, Y. (1988). Shift invariant pattern recognition neural network and its optical architecture. In: Proceedings of the Annual Conference of the Japan Society of Applied Physics, 6p-M-14, 734."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05957-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-021-05957-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05957-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T18:21:05Z","timestamp":1643912465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-021-05957-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,21]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["5957"],"URL":"https:\/\/doi.org\/10.1007\/s10994-021-05957-0","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,21]]},"assertion":[{"value":"9 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2021","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Open Access funding note has been added  to fulfill the contractual requirement of the Switzerland Compact agreement.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}