We present a nonparametric model-agnostic framework for building prediction
intervals of insurance claims, with finite sample statistical guarantees,
extending the technique of split conformal prediction to the domain of
two-stage frequency-severity modeling. The effectiveness of the framework is
showcased with simulated and real datasets. When the underlying severity model
is a random forest, we extend the two-stage split conformal prediction
procedure, showing how the out-of-bag mechanism can be leveraged to eliminate
the need for a calibration set and to enable the production of prediction
intervals with adaptive width.

我们提出了一个非参数模型无关的框架，用于构建保险索赔的预测区间，并给出了统计保证，将分裂拟合预测技术扩展到两阶段频率严重性建模领域。通过模拟和真实数据集展示了该框架的有效性，当基础严重性模型是随机森林时，我们扩展了两阶段分裂拟合预测过程，展示了如何利用袋外机制消除校准集的需求，并能够生成具有自适应宽度的预测区间。

频次 - 严重性建模的一致性预测

Conformal prediction for frequency-severity modeling

Automatically scene understanding using machine learning algorithms has been
widely applied to different industries to reduce the cost of manual labor.
Nowadays, insurance companies launch express vehicle insurance claim and
settlement by allowing customers uploading pictures taken by mobile devices.
This kind of insurance claim is treated as small claim and can be processed
either manually or automatically in a quick fashion. However, due to the
increasing amount of claims every day, system or people are likely to be fooled
by repeated claims for identical case leading to big lost to insurance
companies.Thus, an anti-fraud checking before processing the claim is
necessary. We create the first data set of car damage images collected from
internet and local parking lots. In addition, we proposed an approach to
generate robust deep features by locating the damages accurately and
efficiently in the images. The state-of-the-art real-time object detector YOLO
\cite{redmon2016you}is modified to train and discover damage region as an
important part of the pipeline. Both local and global deep features are
extracted using VGG model\cite{Simonyan14c}, which are fused later for more
robust system performance. Experiments show our approach is effective in
preventing fraud claims as well as meet the requirement to speed up the
insurance claim prepossessing.

使用深度学习算法进行场景理解已被广泛应用于不同的产业领域，现在保险公司通过允许客户上传用手机拍摄的图片来快速处理小额索赔，在处理索赔之前，由于日益增长的索赔量，为避免因同一案件而产生的反复索赔，必须进行防欺诈检查，本文提出一种新的方法，该方法可以定位、高效地生成深度特性，使用我们提出的数据集进行实验，进一步证明了该方法的有效性.