基于客户赔付风险的车险客户价值研究
首发时间:2018-05-31
摘要:利用客户生命周期价值(Customer Lifetime Value,CLV)作为衡量车险客户价值贡献的指标进行有效的续保业务管理,从而将车险的短期交易转变为长期价值。因此,搭建精准的客户价值评估模型至关重要。在传统CLV模型基础上,借鉴金融领域对个别资产进行风险修正的方法,引入客户赔付风险βc系数作为贴现率的调整因子,创建RCLV模型,目的在于解决赔付风险不同的客户在产生同样利润现金流水平下传统CLV模型难以区分高价值低赔付风险客户的问题。RCLV模型的重点在于如何确定βc系数的表现形式。定义βc为单个客户赔付率与整体客户赔付率的比值,并积极探究商业险NCD、交强险NCD、商业三者险限额等从人因子对βc的影响。在βc量化模型的选择上,由于个体赔付率与累积损失趋势一致,且现实中存在大量零索赔保单,零调整回归模型可以进行合理预测。使用某财产保险公司2015年北京地区的车险精算数据,抽取典型样本进行RCLV的测算,并与原始CLV模型测算结果进行对比,说明所建立的模型确实可以避免对潜在优良客户的误判。
关键词: 保险与精算 客户生命周期价值 风险修正 赔付风险 价值区分
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Research on Customer Value of Auto Insurance Based on Customer Compensate Risk
Abstract:Customer Lifetime Value (CLV) is used as an indicator to measure the value contribution of auto insurance customers, and effective renewal business management is carried out, thereby transforming auto insurance short-term transactions into long-term value. Therefore, it is very important to build a precise customer value assessment model. Based on the traditional Research on Customer Value of Auto Insurance Based on Customer Compensate RiskCLV model, this paper draws on the methodResearch on Customer Lifetime Value-based on the adjustment of "beta factor" about compensate risk of risk correction of individual assets in the financial sector, introduces the customer compensate risk factor as an adjustment factor of the discount rate, and creates the RCLV model in order to solve the problem that the CLV model cannot distinguish the potential high-value customers under the same profit cash flow level created by customers with different compensation risks. The RCLV model focuses on how to determine the form of the coefficient. The customer compensate risk factor is defined as the ratio of single customer\'s loss rate to the overall customer\'s loss rate, and actively explores the influence of human factors such as the NCD coefficients of Motor Vehicle Insurance, NCD Coefficients of Motor Vehicle Accident Liability Compulsory Insurance, and limitation of Third-party Liability Insurance of Motor Vehicles. In the selection of quantitative models, since the individual claims ratio is consistent with the cumulative loss trend, and there are a large number of zero claims insurance in reality, the zero-adjustment regression model can be reasonably predicted. This paper uses the auto insurance actuarial data of a property insurance company in Beijing in 2015 to extract typical samples for RCLV measurement, and compares it with the original CLV model calculation results to show that the established model can indeed avoid misjudgment of potential good customers.
Keywords: Insurance and actuarial Customer Lifetime Value Risk-adjusted Compensate risk Value division
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