Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Applying economic measures to lapse risk management with machine learning approaches

Abstract : Modeling policyholders lapse behaviors is important to a life insurer since lapses affect pricing, reserving, profitability, liquidity, risk management, as well as the solvency of the insurer. Lapse risk is indeed the most significant life underwriting risk according to European Insurance and Occupational Pensions Authority's Quantitative Impact Study QIS5. In this paper, we introduce two advanced machine learning algorithms for lapse modeling. Then we evaluate the performance of different algorithms by means of classical statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new for lapse risk management. We apply different algorithms to a large real-world insurance dataset. Our results show that XGBoost and SVM outperform CART and logistic regression, especially in terms of the economic validation metric. The optimization after transformation brings out significant and consistent increases in economic gains.
Complete list of metadatas

Cited literature [47 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02150983
Contributor : Pierrick Piette <>
Submitted on : Friday, December 27, 2019 - 4:54:38 PM
Last modification on : Friday, April 10, 2020 - 5:27:10 PM

File

Manuscript_Lapse_ML.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02150983, version 2
  • ARXIV : 1906.05087

Citation

Stéphane Loisel, Pierrick Piette, Jason Tsai. Applying economic measures to lapse risk management with machine learning approaches. 2019. ⟨hal-02150983v2⟩

Share

Metrics

Record views

73

Files downloads

196