A Neutral network boosted overdispersed Poisson claims reserving model

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<dc:creator>Gabrielli, Andrea</dc:creator>
<dc:description xml:lang="es">Sumario: 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.</dc:description>
<dc:rights xml:lang="es">In Copyright (InC) - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Modelos actuariales</dc:subject>
<dc:subject xml:lang="es">Distribución Poisson-Beta</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Modelos matemáticos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">A Neutral network boosted overdispersed Poisson claims reserving model</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 01/01/2020 Volumen 50 Número 1 - enero 2020 , p. 25-60</dc:relation>