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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 25-Jun-2021 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 https://doi.org/10.1017/asb.2021.10
dc.contributor 風管系
dc.creator (作者) 蔡政憲
dc.creator (作者) Tsai, Jason
dc.creator (作者) Loisel, Stéphane
dc.creator (作者) Piette, Pierrick
dc.date (日期) 2021-06
dc.date.accessioned 25-Jun-2021 09:48:28 (UTC+8)-
dc.date.available 25-Jun-2021 09:48:28 (UTC+8)-
dc.date.issued (上傳時間) 25-Jun-2021 09:48:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/135868-
dc.description.abstract (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.
dc.format.extent 648400 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) ASTIN Bulletin: The Journal of the IAA ,  First View , pp. 1 - 33
dc.title (題名) Applying economic measures to lapse risk management with machine learning approaches
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1017/asb.2021.10
dc.doi.uri (DOI) https://doi.org/10.1017/asb.2021.10