A NEURAL NETWORK BOOSTED DOUBLE OVERDISPERSED POISSON CLAIMS RESERVING MODEL

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Probability and Risk Models Insurance and Financial Risk Management Insurance, Mortality, Demography, Risk Management

Abstract

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.

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References

1993Distribution-free Calculation of the Standard Error of Chain Ladder Reserve Estimates 2002Stochastic Claims Reserving in General Insurance 1998A Stochastic Model Underlying the Chain-Ladder Technique 1991A Simple Parametric Model for Rating Automobile Insurance or Estimating IBNR Claims Reserves 2018Neural Network Embedding of the Over-Dispersed Poisson Reserving Model 2019DeepTriangle: A Deep Learning Approach to Loss Reserving 2018An Individual Claims History Simulation Machine 2012Double Chain Ladder

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