Meta-Models and Consistency Issues

Laurent, Jean Paul

doi:10.1007/978-3-319-29776-7_9

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Step 1: Modeling R Acceptant and R Lapser1

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2023

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“…This estimation is more complex as we must dissociate the risks of lapse and death. These causes being mutually exclusive, a competing risks methodology is wellsuited to estimate r acceptant [43].…”

Section: Step 1: Modeling R Acceptant and R Lapsermentioning

confidence: 99%

Including individual customer lifetime value and competing risks in tree-based lapse management strategies

Valla,

Milhaud,

Olympio

2023

Eur. Actuar. J.

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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.

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Section: Step 1: Modeling R Acceptant and R Lapsermentioning

confidence: 99%