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A Neutral network boosted overdispersed Poisson claims reserving model

Recurso electrónico / Electronic resource
MARC record
Tag12Value
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001  MAP20200009900
003  MAP
005  20200326142041.0
008  200326e20200101bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20200006558‎$a‎Gabrielli, Andrea
24512‎$a‎A Neutral network boosted overdispersed Poisson claims reserving model‎$c‎Andrea Gabrielli
520  ‎$a‎We 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.
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20090041721‎$a‎Distribución Poisson-Beta
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080592042‎$a‎Modelos matemáticos
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/01/2020 Volumen 50 Número 1 - enero 2020 , p. 25-60