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A NEURAL NETWORK BOOSTED DOUBLE OVERDISPERSED POISSON CLAIMS RESERVING MODEL
ASTIN Bulletin: The Journal of the IAA ( IF 1.9 ) Pub Date : 2019-12-17 , DOI: 10.1017/asb.2019.33 Andrea Gabrielli
ASTIN Bulletin: The Journal of the IAA ( IF 1.9 ) Pub Date : 2019-12-17 , DOI: 10.1017/asb.2019.33 Andrea Gabrielli
We present an actuarial claims reserving technique that takes into account both claim counts and claim amounts. Separate (overdispersed) Poisson models for the claim counts and the claim amounts are combined by a joint embedding into a neural network architecture. As starting point of the neural network calibration, we use exactly these two separate (overdispersed) Poisson models. Such a nested model can be interpreted as a boosting machine. It allows us for joint modeling and mutual learning of claim counts and claim amounts beyond the two individual (overdispersed) Poisson models.
中文翻译:
基于神经网络的双重过度泊松索赔模型
我们提出了一种精算索赔准备金技术,该技术同时考虑了索赔数和索赔额。通过联合嵌入神经网络体系结构,将索赔数量和索赔金额分开(过度分散)的泊松模型组合在一起。作为神经网络校准的起点,我们恰好使用了这两个单独的(过度分散的)泊松模型。这样的嵌套模型可以解释为助推器。它使我们能够进行联合建模,并在两个单独的(过度分散的)泊松模型之外对索赔计数和索赔额进行相互学习。
更新日期:2020-04-18
中文翻译:
基于神经网络的双重过度泊松索赔模型
我们提出了一种精算索赔准备金技术,该技术同时考虑了索赔数和索赔额。通过联合嵌入神经网络体系结构,将索赔数量和索赔金额分开(过度分散)的泊松模型组合在一起。作为神经网络校准的起点,我们恰好使用了这两个单独的(过度分散的)泊松模型。这样的嵌套模型可以解释为助推器。它使我们能够进行联合建模,并在两个单独的(过度分散的)泊松模型之外对索赔计数和索赔额进行相互学习。