Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311903 
Year of Publication: 
2022
Citation: 
[Journal:] European Actuarial Journal [ISSN:] 2190-9741 [Volume:] 13 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 541-569
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Lapse risk is a key risk driver for life and pensions business with a material impact on the cash flow profile and the profitability. The application of data science methods can replace the largely manual and time-consuming process of estimating a lapse model that reflects various contract characteristics and provides best estimate lapse rates, as needed for Solvency II valuations. In this paper, we use the Lasso method which is based on a multivariate model and can identify patterns in the data set automatically. To identify hidden structures within covariates, we adapt and combine recently developed extended versions of the Lasso that apply different sub-penalties for individual covariates. In contrast to random forests or neural networks, the predictions of our lapse model remain fully explainable, and the coefficients can be used to interpret the lapse rate on an individual contract level. The advantages of the method are illustrated based on data from a European life insurer operating in four countries. We show how structures can be identified efficiently and fed into a highly competitive, automatically calibrated lapse model.
Subjects: 
Lapse rate
Life insurance
Lasso
Fused Lasso
Trend filtering
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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