Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit

Bing He, Dian Zhang, Siyuan Liu, Hao Liu, Dawei Han, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Profiling driver behaviors and designing appropriate pricing models are essential for auto insurance companies to gain profits and attract customers (drivers). The existing approaches either rely on static demographic information like age, or model only coarse-grained driving behaviors. They are therefore ineffective to yield accurate risk predictions over time for appropriate pricing, resulting in profit decline or even financial loss. Moreover, existing pricing strategies seldom take profit maximization into consideration, especially under the enterprise constraints. The recent growth of vehicle telematics data (vehicle sensing data) brings new opportunities to auto insurance industry, because of its sheer size and fine-grained mobility for profiling drivers. But, how to fuse these sparse, inconsistent and heterogeneous data is still not well addressed. To tackle these problems, we propose a unified PPP (Profile-Price-Profit) framework, working on the real-world large-scale vehicle telematics data and insurance data. PPP profiles drivers' fine-grained behaviors by considering various driving features from the trajectory perspective. Then, to predict drivers' risk probabilities, PPP leverages the group-level insight and categorizes drivers' different temporal risk change patterns into groups by ensemble learning. Next, the pricing model in PPP incorporates both the demographic analysis and the mobility factors of driving risk and mileage, to generate personalized insurance price for supporting flexible premium periods. Finally, the maximal profit problem proves to be NP-Complete. Then, an efficient heuristic-based dynamic programming is proposed. Extensive experimental results demonstrated that, PPP effectively predicts the driver's risk and outperforms the current company's pricing strategy (in industry) and the state-of-the-art approach. PPP also achieves near the maximal profit (difference by only 3%) for the company, and lowers the total price for the drivers.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1387-1396
Number of pages10
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

Fingerprint

Insurance
Profitability
Costs
Industry
Electric fuses
Dynamic programming
Trajectories

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Cite this

He, B., Zhang, D., Liu, S., Liu, H., Han, D., & Ni, L. M. (2019). Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, ... X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 1387-1396). [8622491] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622491
He, Bing ; Zhang, Dian ; Liu, Siyuan ; Liu, Hao ; Han, Dawei ; Ni, Lionel M. / Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Yang Song ; Bing Liu ; Kisung Lee ; Naoki Abe ; Calton Pu ; Mu Qiao ; Nesreen Ahmed ; Donald Kossmann ; Jeffrey Saltz ; Jiliang Tang ; Jingrui He ; Huan Liu ; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1387-1396 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).
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He, B, Zhang, D, Liu, S, Liu, H, Han, D & Ni, LM 2019, Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit. in Y Song, B Liu, K Lee, N Abe, C Pu, M Qiao, N Ahmed, D Kossmann, J Saltz, J Tang, J He, H Liu & X Hu (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622491, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1387-1396, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8622491

Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit. / He, Bing; Zhang, Dian; Liu, Siyuan; Liu, Hao; Han, Dawei; Ni, Lionel M.

Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1387-1396 8622491 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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He B, Zhang D, Liu S, Liu H, Han D, Ni LM. Profiling Driver Behavior for Personalized Insurance Pricing and Maximal Profit. In Song Y, Liu B, Lee K, Abe N, Pu C, Qiao M, Ahmed N, Kossmann D, Saltz J, Tang J, He J, Liu H, Hu X, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1387-1396. 8622491. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). https://doi.org/10.1109/BigData.2018.8622491