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

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  • 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
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