机器学习技术在保单失复效管理工作中的应用

Application of Machine Learning Technology in the Management of Lapse and Reinstatement of Policy

为了保障保险公司长期稳健发展,保单管理工作须采取现代科技手段加以完善。文章通过对保险公司历史上大量不同时期失效保单特征进行分析,研究构建了基于Stacking多模型融合技术保单失复效模型。该模型能预测失效保单客户在短期内复效概率,并制定相关复效督导管理策略和差异化服务策略。通过基于帕累托法则的实证分析,采用Stacking多模型融合技术构建的失复效模型较其他集成算法构建优势明显,特别是将模型融入PDCA管理实践中,能够为保单精细化管理提供有效的数据支撑,具备良好业务价值。

In order to ensure the long-term and stable development of insurance companies,the management of policies must be improved by modern technology.The article analyzes the characteristics of a large number of lapsed policies in different periods in the history of insurance companies,and researches and builds a policy lapse and reinstatement model based on Stacking multi-model fusion technology.The model can predict the recovery probability of lapsed policy customers in the short term,and formulate related recovery supervision and management strategy and differentiated service strategy.Through empirical analysis based on the Pareto principle,the lapse and reinstatement model constructed by using Stacking multi-model fusion technology has obvious advantages over other integrated algorithm construction.In particular,the model is integrated into PDCA management practice,which can provide effective data support for the refined management of insurance policies and has good business value.