The aim of the paper is to derive a simple, implementable machine learning method for general insurance losses. An algorithm for learning a general insurance loss triangle is developed and justified. An argument is made for applying support vector regression (SVR) to this learning task (in order to facilitate transparency of the learning method as compared to more “black-box” methods such as deep neural networks), and SVR methodology derived is specifically applied to this learning task. A further argument for preserving the statistical features of the loss data in the SVR machine is made. A bespoke kernel function that preserves the statistical features of the loss data is derived from first principles and called the exponential dispersion family (EDF) kernel. Features of the EDF kernel are explored, and the kernel is applied to an insurance loss estimation exercise for homogeneous risk of three different insurers. Results of the cumulative losses and ultimate losses predicted by the EDF kernel are compared to losses predicted by the radial basis function kernel and the chain-ladder method. A backtest of the developed method is performed. A discussion of the results and their implications follows.

中文翻译：

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一种实用的支持向量回归算法和核函数用于损耗性一般保险损失估计

本文的目的是为一般保险损失推导出一种简单、可实施的机器学习方法。开发并证明了一种用于学习一般保险损失三角形的算法。提出了将支持向量回归 (SVR) 应用于此学习任务的论点（与深度神经网络等更多“黑盒”方法相比，以促进学习方法的透明度），并专门应用了 SVR 方法派生到这个学习任务。进一步论证了在 SVR 机器中保留损失数据的统计特征。保留损失数据统计特征的定制核函数源自第一原理，称为指数色散族 (EDF) 核。探索了 EDF 内核的特性，内核应用于三个不同保险公司的同质风险的保险损失估计练习。将 EDF 核预测的累积损失和最终损失的结果与径向基函数核和链梯法预测的损失进行比较。对所开发的方法进行回测。以下是对结果及其含义的讨论。