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A Neural Network Boosted Double Over-Dispersed Poisson Claims Reserving Model
Andrea Gabrielli
出版
SSRN
, 2019
URL
http://books.google.com.hk/books?id=-3cAzwEACAAJ&hl=&source=gbs_api
註釋
We present an actuarial loss reserving technique that takes into account both claim counts and claim amounts. Separate (over-dispersed) 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 (over-dispersed) 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 (over-dispersed) Poisson models. Moreover, this choice of neural network initialization guarantees stability and accelerates representation learning.