Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance

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<title>Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance</title>
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<namePart>Rosenstock, Alexander </namePart>
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<abstract displayLabel="Summary">Usually, the actuarial problems of predicting the number of claims incurred but not reported (IBNR) and of modelling claim frequencies are treated successively by insurance companies. New micro-level methods designed for large datasets are proposed that address the two problems simultaneously. The methods are based on an elaborated occurrence process model that includes both a claim intensity model and a claim development model. The influence of claim feature variables is modelled by suitable neural networks. Extensive simulation experiments and a case study on a large real data set from a motor legal insurance portfolio show accurate predictions at both the aggregate and individual policy level, as well as appropriate fitted models for claim frequencies. Moreover, a novel alternative approach combining data from classic triangle-based methods with a micro-level intensity model is introduced and compared to the full micro-level approach</abstract>
<note type="statement of responsibility">Axel Bücher & Alexander Rosenstock</note>
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<topic>Seguro de salud</topic>
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<topic>Mercado de seguros</topic>
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<topic>Siniestros</topic>
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<topic>Seguros no vida</topic>
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<topic>Cálculo actuarial</topic>
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<topic>Automóviles</topic>
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<topic>Seguro de automóviles</topic>
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<url displayLabel="electronic resource" usage="primary display">https://link.springer.com/article/10.1007/s13385-024-00383-7</url>
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<title>European Actuarial Journal</title>
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<publisher>Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</publisher>
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<identifier type="local">MAP20220007085</identifier>
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<text>15/08/2024 Volumen 14 - Número 2 - agosto 2024 , P. 623-655</text>
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