摘要
针对寿险公司偿付能力影响因素以及预警管理研究,选取了不同规模的14家寿险公司流动比率、赔付率、投资收益率、总资产回报率、再保险率、保费收益率、保费变化率、寿险准备金增长率8个指标的相关数据,并利用变异系数法和相关系数法分别筛选影响较大的因素,再逐步回归得到基于主要影响因素的偿付能力回归方程,以此进行因素分析.在指标优化的基础上,利用BP神经网络模型建立寿险公司偿付能力的预警系统,同时与广义Logistic模型预测结果进行对比,得出BPNN具有更准确的判别预警效果,为寿险公司偿付能力预警以及投保人降低自身损失作出参考作用.
In view of the influencing factors of life insurance company’s solvency and early warning management research, 14 life insurance companies with different scales were selected for current ratio, loss ratio, investment return rate, total return on assets, reinsurance rate, premium rate of return, premium rate of change and life insurance. The relevant data of 8 indicators of reserve growth rate are used, and the factors with greater influence are screened by the coefficient of variation method and the correlation coefficient method respectively, and then the regression equation of solvency based on the main influencing factors is gradually obtained to analyze the factors. Based on the optimization of indicators, the BP neural network model is used to establish the early warning system of life insurance compan’’s solvency. At the same time, compared with the generalized Logistic model, the BPNN has a more accurate early warning effect, and it is the life insurance compan’’s solvency warning and insurance. People reduce their own losses to make reference.
作者
李丹
朱家明
Li Dan;Zhu Jiaming(Anhui University of Finance and Economics)
出处
《哈尔滨师范大学自然科学学报》
CAS
2019年第1期6-11,共6页
Natural Science Journal of Harbin Normal University
基金
国家自然科学项目(61305070)
安徽省教研项目(2016ckjh007)
