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Machine Learning Approaches for Auto Insurance Big Data

Author

Listed:
  • Mohamed Hanafy

    (School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China
    Department of Statistics, Mathematics, and Insurance, Faculty of commerce, Assuit University, Asyut 71515, Egypt)

  • Ruixing Ming

    (School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.

Suggested Citation

  • Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:2:p:42-:d:502813
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    References listed on IDEAS

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    1. K A Smith & R J Willis & M Brooks, 2000. "An analysis of customer retention and insurance claim patterns using data mining: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 532-541, May.
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    3. Hultkrantz, Lars & Nilsson, Jan-Eric & Arvidsson, Sara, 2012. "Voluntary internalization of speeding externalities with vehicle insurance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(6), pages 926-937.
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    Cited by:

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    2. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    3. Panyi Dong & Zhiyu Quan & Brandon Edwards & Shih-han Wang & Runhuan Feng & Tianyang Wang & Patrick Foley & Prashant Shah, 2024. "Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry," Papers 2402.14983, arXiv.org.
    4. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    5. Allen R. Williams & Yoolim Jin & Anthony Duer & Tuka Alhani & Mohammad Ghassemi, 2022. "Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach," Risks, MDPI, vol. 10(6), pages 1-17, June.
    6. Sebastian Baran & Przemys{l}aw Rola, 2022. "Prediction of motor insurance claims occurrence as an imbalanced machine learning problem," Papers 2204.06109, arXiv.org.
    7. Codruţa Mare & Daniela Manaţe & Gabriela-Mihaela Mureşan & Simona Laura Dragoş & Cristian Mihai Dragoş & Alexandra-Anca Purcel, 2022. "Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    8. Shengkun Xie & Rebecca Luo & Yuanshun Li, 2022. "Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data," Risks, MDPI, vol. 10(10), pages 1-21, October.

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