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Detection of interacting variables for generalized linear models via neural networks

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      <subfield code="a">Havrylenko, Yevhen </subfield>
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      <subfield code="a">Detection of interacting variables for generalized linear models via neural networks</subfield>
      <subfield code="c">Yevhen Havrylenko & Julia Heger</subfield>
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      <subfield code="a">In this paper, we propose a methodology for the detection of the next-best interaction that is missing in a benchmark GLM. We aim at improving an arbitrary but fixed existing benchmark GLM instead of creating a new GLM from scratch. Building a new GLM may necessitate drastic changes in the tariff of the MTPL insurance. Large changes in tariffs are not desired by insurance companies for their existing business lines. Instead, GLMs need to be improved gradually</subfield>
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      <subfield code="a">Heger, Julia </subfield>
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      <subfield code="g">15/08/2024 Volumen 14 - Número 2 - agosto 2024 , p. 551-580</subfield>
      <subfield code="t">European Actuarial Journal</subfield>
      <subfield code="d">Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</subfield>
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      <subfield code="u">https://link.springer.com/article/10.1007/s13385-023-00362-4</subfield>
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