论文标题
个人主张预测贝叶斯混合物密度网络
Individual Claims Forecasting with Bayesian Mixture Density Networks
论文作者
论文摘要
我们介绍了一个个人主张的预测框架，利用贝叶斯混合物密度网络，可用于索赔分析任务，例如案例保留和分盘。所提出的方法可以使从结构化和非结构化数据源中纳入索赔信息，产生多周期现金流量预测，并产生未来付款方式的不同情况。我们使用公开可用的数据实施和评估建模框架。
We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging. The proposed approach enables incorporating claims information from both structured and unstructured data sources, producing multi-period cash flow forecasts, and generating different scenarios of future payment patterns. We implement and evaluate the modeling framework using publicly available data.
