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政大機構典藏 > 商學院 > 風險管理與保險學系 > 期刊論文 > Item 140.119/135868

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題名:	Applying economic measures to lapse risk management with machine learning approaches
作者:	蔡政憲 Tsai, Jason Loisel, Stéphane Piette, Pierrick
貢獻者:	風管系
日期:	2021-06
上傳時間:	2021-06-25 09:48:28 (UTC+8)
摘要:	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.
關聯:	ASTIN Bulletin: The Journal of the IAA , First View , pp. 1 - 33
資料類型:	article
DOI 連結:	https://doi.org/10.1017/asb.2021.10
DOI:	10.1017/asb.2021.10
顯示於類別:	[風險管理與保險學系] 期刊論文

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