  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.
