A Neutral network boosted overdispersed Poisson claims reserving model

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<title>Neutral network boosted overdispersed Poisson claims reserving model</title>
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<abstract displayLabel="Summary">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.</abstract>
<note type="statement of responsibility">Andrea Gabrielli</note>
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<topic>Modelos actuariales</topic>
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<topic>Distribución Poisson-Beta</topic>
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<topic>Cálculo actuarial</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
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<text>01/01/2020 Volumen 50 Número 1 - enero 2020 , p. 25-60</text>
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