Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction is one such method that can meet this need, and here we tailor that approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.

中文翻译：

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未来保险索赔的有效无模型预测

在预测保险索赔时，由模型错误指定导致的偏差是一个问题。事实上，这种偏见使保险公司面临做出无效或不可靠预测的风险。一种可以在一大类可能的分布中一致地提供可证明有效的预测的方法将有效地消除模型错误指定偏差的风险。保形预测是一种可以满足这种需求的方法，在这里我们针对典型的保险应用定制该方法，并表明预测不仅有效而且在广泛的设置中也有效。