IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v9y2021i2p42-d502813.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/9/2/42/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/9/2/42/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    2. Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    3. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    4. Mercedes Ayuso & Montserrat Guillen & Ana María Pérez-Marín, 2016. "Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s," Risks, MDPI, vol. 4(2), pages 1-10, April.
    5. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    6. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    7. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    8. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    9. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    10. Dementyeva, Maria & Koster, Paul R. & Verhoef, Erik T., 2015. "Regulation of road accident externalities when insurance companies have market power," Journal of Urban Economics, Elsevier, vol. 86(C), pages 1-8.
    11. Antonino Mario Oliveri & Gabriella Polizzi & Anna Maria Parroco, 2019. "Measuring Tourist Satisfaction Through a Dual Approach: The 4Q Methodology," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 361-382, November.
    12. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    13. Paolo Castelnovo & Martina Dal Molin, 2021. "The learning mechanisms through public procurement for innovation: The case of government‐funded basic research organizations," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(3), pages 411-446, September.
    14. Violetta Simonacci & Michele Gallo, 2017. "Statistical tools for student evaluation of academic educational quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 565-579, March.
    15. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    16. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
    17. Dutang, Christophe & Albrecher, Hansjoerg & Loisel, Stéphane, 2013. "Competition among non-life insurers under solvency constraints: A game-theoretic approach," European Journal of Operational Research, Elsevier, vol. 231(3), pages 702-711.
    18. David L. Olson, 2007. "Data mining in business services," Service Business, Springer;Pan-Pacific Business Association, vol. 1(3), pages 181-193, September.
    19. Bian, Yiyang & Yang, Chen & Zhao, J. Leon & Liang, Liang, 2018. "Good drivers pay less: A study of usage-based vehicle insurance models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 20-34.
    20. Joseph Levitas & Konstantin Yavilberg & Oleg Korol & Genadi Man, 2022. "Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction," Papers 2212.09385, arXiv.org, revised Mar 2023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:9:y:2021:i:2:p:42-:d:502813. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.