Evaluating Telematics-Driven Experience Variables in Auto Insurance Risk Modeling via Interpretable Machine Learning
Keywords:
Telematics, Auto Insurance, Interpretable Machine Learning, Risk Modeling, Usage-Based InsuranceAbstract
Telematics devices generate continuous streams of driving behavior data that can enrich auto insurance risk assessment beyond traditional demographic and policy variables. This study evaluates the incremental value of telematics-driven experience variables for predicting claim occurrence and claim severity in personal auto insurance. Using a large proprietary dataset of anonymized policyholders, we construct experience variables including harsh braking frequency, night driving proportion, speeding exposure, and monthly mileage. We compare conventional logistic regression with tree-based machine learning models, including random forest, gradient boosting, and light gradient boosting. To address the opacity of complex models, we apply interpretable machine learning techniques such as SHAP values, permutation importance, and partial dependence analysis. The experimental results show that gradient boosting achieves the strongest discrimination, with an area under the receiver operating characteristic curve of 0.819, compared with 0.742 for logistic regression. Global explanations reveal that harsh braking frequency and night driving proportion are the most influential telematics experience variables, both associated with increased risk. Monthly mileage exhibits a weaker but protective association after controlling for other behavioral signals. Local explanations further indicate that interaction effects between speeding exposure and night driving amplify predicted risk for a subset of high-risk drivers. The findings suggest that telematics experience variables improve risk ranking while remaining interpretable through post hoc explanation methods. The study contributes a transparent modeling workflow that can support actuarial pricing, underwriting triage, and usage-based insurance design. Limitations include reliance on a single insurer dataset and potential behavioral adaptation. Overall, interpretable machine learning offers a practical pathway for insurers to integrate telematics experience variables without sacrificing model accountability or regulatory defensibility.References
1. M. Siami, M. Naderpour, and J. Lu, “A mobile telematics pattern recognition framework for driving behavior extraction,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1459–1472, 2020.
2. I. Reimers and B. R. Shiller, “The impacts of telematics on competition and consumer behavior in insurance,” Journal of Law and Economics, vol. 62, no. 4, pp. 613–632, 2019.
3. G. Gao, S. Meng, and M. V. Wüthrich, “What can we learn from telematics car driving data: A survey,” Insurance: Mathematics and Economics, vol. 104, pp. 185–199, 2022.
4. R. Ascott, “Art and telematics: Towards a network consciousness,” in Art Telecommunication. Vancouver, BC, Canada: The Western Front, 1984, pp. 25–67.
5. N. Arunkumar and S. S. Yellampalli, “Disruptive technology for auto insurance entrepreneurs: New paradigm using telematics and machine learning,” in Proc. Int. Conf. Smart Technologies For Smart Nation (SmartTechCon), Aug. 2017, pp. 195–200.
6. J. P. Boucher, S. Côté, and M. Guillen, “Exposure as duration and distance in telematics motor insurance using generalized additive models,” Risks, vol. 5, no. 4, p. 54, 2017.
7. F. Usman, J. S. Chan, U. E. Makov, Y. Wang, and A. X. Dong, “Claim prediction and premium pricing for telematics auto insurance data using poisson regression with lasso regularisation,” Risks, vol. 12, no. 9, p. 137, 2024.
8. N. Hu, “A data science approach for evaluating experience variables in insurance risk modeling,” in Proc. IEEE Int. Conf. Information Reuse and Integration for Data Science (IRI), 2026, pp. 282–287.
9. M. Guillen, A. M. Pérez-Marín, and J. P. Nielsen, “Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data,” Heliyon, vol. 10, no. 16, 2024.
10. Y. Luo, J. Xu, Y. Chen, et al., “Optimal hovering control strategy for UAVs using PPO,” in *Proc. 7th Int. Conf. Machine Learning, Big Data and Business Intelligence (MLBDBI)*. Piscataway, NJ, USA: IEEE, 2025, pp. 247–251.
11. F. Duval, J. P. Boucher, and M. Pigeon, “How much telematics information do insurers need for claim classification?,” North American Actuarial Journal, vol. 26, no. 4, pp. 570–590, 2022.
12. P. S. Sreethamol, K. S. Sulfiya, and K. M. Vineeth, “Consumers perception towards telematics in insurance,” Research Journal of Humanities and Social Sciences, vol. 9, no. 3, pp. 657–662, 2018.
13. S. Husnjak, D. Peraković, I. Forenbacher, and M. Mumdziev, “Telematics system in usage based motor insurance,” Annals of DAAAM & Proceedings, vol. 25, no. 1, 2014.
14. L. Ignatovich, “Use of innovative technologies by insurance market entities. Telematics in car insurance,” Three Seas Economic Journal, vol. 1, no. 2, pp. 7–11, 2020.
15. P. Nijkamp, G. Pepping, and D. Banister, Telematics and Transport Behaviour. Berlin, Germany: Springer Science & Business Media, 2012.
16. S. Duri, M. Gruteser, X. Liu, P. Moskowitz, R. Perez, M. Singh, and J. M. Tang, “Framework for security and privacy in automotive telematics,” in Proc. 2nd Int. Workshop Mobile Commerce, Sep. 2002, pp. 25–32.
17. J. Zhang and H. Gao, “LLM inference delay prediction based on machine learning algorithms,” in *Proc. Int. Conf. Generative Artificial Intelligence and Information Security (GAIIS)*. Piscataway, NJ, USA: IEEE, 2026.
18. J. Wahlström, I. Skog, and P. Händel, “Driving behavior analysis for smartphone-based insurance telematics,” in Proc. 2nd Workshop Physical Analytics, May 2015, pp. 19–24.
19. M. C. Sosa-Iudicissa, Ed., Internet, Telematics, and Health, vol. 36. Amsterdam, The Netherlands: IOS Press, 1997.
20. Q. Jiang and T. Shi, “Auto insurance pricing using telematics data: Application of a hidden markov model,” North American Actuarial Journal, vol. 28, no. 4, pp. 822–839, 2024.
21. A. Cevolini, E. Morotti, E. Esposito, L. Romanelli, R. Tisseur, and C. Misani, “Can telematics improve driving style? The use of behavioral data in motor insurance,” Big Data and Cognitive Computing, vol. 9, no. 9, p. 225, 2025.
22. C. C. Constantinescu, I. Stancu, and I. Panait, “Impact study of telematics auto insurance,” Review of Financial Studies, vol. 3, pp. 17–35, 2018.
23. A. Kalašová and J. Kučuljaková, “The future in the telematics applications as support for increased safety,” Transport Problems, vol. 7, no. 1, pp. 103–109, 2012.
24. D. Clark, B. Ilbery, and N. Berkeley, “Telematics and rural businesses: an evaluation of uses, potentials and policy implications,” Regional Studies, vol. 29, no. 2, pp. 171–180, 1995.
25. D. Yoon, J. Choi, H. Kim, and J. Kim, “Future automotive insurance system based on telematics technology,” in Proc. 10th Int. Conf. Advanced Communication Technology, vol. 1, Feb. 2008, pp. 679–681.
26. X. Li, Z. Guo, and R. Liu, “Task-Adaptive Calibration for Multimodal Long-Context Extension,” Research Square, preprint, Aug. 23, 2026. doi: 10.21203/rs.3.rs-10769418/v1.
27. M. Guillen, J. P. Nielsen, and A. M. Pérez-Marín, “Near-miss telematics in motor insurance,” Journal of Risk and Insurance, vol. 88, no. 3, pp. 569–589, 2021.
28. M. Guillen, “Motor insurance with telematics driving data,” 2022.
29. M. A. Quddus, W. Y. Ochieng, L. Zhao, and R. B. Noland, “A general map matching algorithm for transport telematics applications,” GPS Solutions, vol. 7, no. 3, pp. 157–167, 2003.
30. W. Guo, W. Zhang, S. Wang, and R. Liu, “Efficient Test-Time Adaptation for Dynamic Distribution Shifts,” 2026.
31. S. Xie, “Analyzing the influence of telematics-based pricing strategies on traditional rating factors in auto insurance rate regulation,” Mathematics, vol. 12, no. 19, p. 3150, 2024.
32. L. Longhi and M. Nanni, “Car telematics big data analytics for insurance and innovative mobility services,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 3989–3999, 2020.
33. M. Guillen, J. P. Nielsen, M. Ayuso, and A. M. Pérez-Marín, “The use of telematics devices to improve automobile insurance rates,” Risk Analysis, vol. 39, no. 3, pp. 662–672, 2019.
34. L. Guan, J. Xu, S. Wang, X. Xing, L. Lin, H. Huang, and W. Lee, “Exploiting insurance telematics for fun and profit,” arXiv preprint arXiv:1609.02234, 2016.
35. M. Guillen, J. P. Nielsen, A. M. Pérez-Marín, and V. Elpidorou, “Can automobile insurance telematics predict the risk of near-miss events?,” North American Actuarial Journal, vol. 24, no. 1, pp. 141–152, 2020.
