Title: Lapse risk modelling in insurance: a Bayesian mixture approach
Wednesdays, September 4, from 3:30 p.m. to 5:00 p.m. (Rio de Janeiro local time)
Speaker: Viviana Lobo (IM-UFRJ)
Local: Laboratório de Sistemas Estocásticos (LSE), Sala I-044-B, Centro de Tecnologia - UFRJ
Abstract: This paper focuses on modelling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modelling of the time to cancellation must account for this specific behaviour. Another stylised fact is that policies which are not cancelled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. An illustrative example emulates a typical behaviour for life insurance contracts and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components In particular, the observed censoring in the insurance context might be up to 50% of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study.
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