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

TL;DR

This paper introduces a novel lapse management framework incorporating Customer Lifetime Value and competing risks, demonstrating that survival tree models outperform parametric methods in predicting lapses and enhancing profitability for life insurers.

Contribution

The paper develops a new framework for lapse management that integrates individual CLV and competing risks, with survival tree models showing superior performance over traditional parametric approaches.

Findings

Survival tree models outperform parametric models in lapse prediction.

Inclusion of CLV and competing risks improves profitability predictions.

Framework leads to increased predicted gains for insurers.

Abstract

A retention strategy based on an enlightened lapse model is a powerful profitabilitylever 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 notenough to design a retention strategy. In our paper, we define a lapse managementframework with an appropriate validation metric based on Customer Lifetime Value andprofitability. We include the risk of death in the study through competing risks considerations inparametric and tree-based models and show that further individualization of theexisting approaches leads to increased performance. We show that survival tree-basedmodels outperform parametric approaches and that the actuarial literature cansignificantly benefit from them. Then, we compare, on real data, how this frameworkleads to increased predicted gains for a life insurer and discuss the benefits of ourmodel in terms of commercial and strategic decision-making.