IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v45y2026i2p652-669.html

Innovative Techniques to Predict Churn in the French Insurance Industry: Integration of Machine Learning With the Grabit Model

Author

Listed:
  • Christophe Schalck
  • Meryem Yankol‐Schalck

Abstract

The aim of this study is to identify the characteristics of policyholders that may indicate a risk of cancellation in the French insurance sector over the period 2013–2016. Customer churn predictions are provided by using traditional data mining methods (Tobit model), machine learning methods (XGBoost algorithm), and a hybrid of machine learning and data mining methods (Grabit model). Results show that the Grabit model outperforms the Tobit model and XGBoost algorithm according to selected performance metrics. The study revealed that the characteristics of clients (age, marital status) and the characteristics of the policies (type, number of policies, payment mode, online channel, timing of policy cancellations) significantly influence client departure. They allow for better decision‐making and the implementation of relevant marketing strategies.

Suggested Citation

  • Christophe Schalck & Meryem Yankol‐Schalck, 2026. "Innovative Techniques to Predict Churn in the French Insurance Industry: Integration of Machine Learning With the Grabit Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 652-669, March.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:2:p:652-669
    DOI: 10.1002/for.70057
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.70057
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.70057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:wly:jforec:v:45:y:2026:i:2:p:652-669. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.