Including individual Customer Lifetime Value and competing risks in tree-based lapse management strategies

Mathias Valla (), Xavier Milhaud () and Anani Ayodélé Olympio ()
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Mathias Valla: LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, Faculty of Business and Economics - University of Leuven (KUL)
Xavier Milhaud: I2M - Institut de Mathématiques de Marseille - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Anani Ayodélé Olympio: LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon

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Abstract: A retention strategy based on an enlightened lapse model is a powerful profitability lever for a life insurer. Some machine learning models are excellent at predicting lapse, but from the insurer's perspective, predicting which policyholder is likely to lapse is not enough to design a retention strategy. In our paper, we define a lapse management framework with an appropriate validation metric based on Customer Lifetime Value and profitability. We include the risk of death in the study through competing risks considerations in parametric and tree-based models and show that further individualization of the existing approaches leads to increased performance. We show that survival tree-based models outperform parametric approaches and that the actuarial literature can significantly benefit from them. Then, we compare, on real data, how this framework leads to increased predicted gains for a life insurer and discuss the benefits of our model in terms of commercial and strategic decision-making.

Keywords: Machine Learning; Life insurance; Customer lifetime value; Lapse; Lapse management strategy; Competing risks; Tree-based models (search for similar items in EconPapers)
Date: 2023-09-12
New Economics Papers: this item is included in nep-big and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03903047v4
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Citations: View citations in EconPapers (1)

Published in European Actuarial Journal, 2023, ⟨10.1007/s13385-023-00358-0⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03903047

DOI: 10.1007/s13385-023-00358-0

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