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A NEURAL NETWORK BOOSTED DOUBLE OVERDISPERSED POISSON CLAIMS RESERVING MODEL

Published online by Cambridge University Press:  17 December 2019

Andrea Gabrielli
Andrea Gabrielli*
Affiliation:
RiskLab, Department of Mathematics, ETH Zurich, E-MAIL: andrea.gabrielli@math.ethz.ch
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Abstract

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.

Keywords

Cross-classified overdispersed Poisson modelclaims reserving in insurancechain–ladder reservesneural networkembeddingclaim countsclaim amountslearning across portfoliosboosting
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 1 , January 2020 , pp. 25 - 60
Copyright
© Astin Bulletin 2019 

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