Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data

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      <subfield code="a">Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data</subfield>
      <subfield code="c">Francis Duval, Jean-Philippe Boucher, Mathieu Pigeon</subfield>
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      <subfield code="a">We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part</subfield>
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      <subfield code="g">15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.239-262</subfield>
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      <subfield code="u">https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/telematics-combined-actuarial-neural-networks-for-crosssectional-and-longitudinal-claim-count-data/B6C01BF508F64D4C7A804A628F3D03E0</subfield>
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