A Neutral network boosted overdispersed Poisson claims reserving model

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<subfield code="a">Gabrielli, Andrea</subfield>
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<subfield code="a">A Neutral network boosted overdispersed Poisson claims reserving model</subfield>
<subfield code="c">Andrea Gabrielli</subfield>
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<subfield code="a">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.</subfield>
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<subfield code="a">Modelos actuariales</subfield>
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<subfield code="a">Distribución Poisson-Beta</subfield>
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<subfield code="a">Cálculo actuarial</subfield>
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<subfield code="a">Modelos matemáticos</subfield>
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<subfield code="t">Astin bulletin</subfield>
<subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
<subfield code="x">0515-0361</subfield>
<subfield code="g">01/01/2020 Volumen 50 Número 1 - enero 2020 , p. 25-60</subfield>