Detection of interacting variables for generalized linear models via neural networks

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<dc:creator>Havrylenko, Yevhen </dc:creator>
<dc:creator>Heger, Julia </dc:creator>
<dc:date>2024-08-15</dc:date>
<dc:description xml:lang="es">Sumario: 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</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/186138.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Mercado de seguros</dc:subject>
<dc:subject xml:lang="es">Empresas de seguros</dc:subject>
<dc:subject xml:lang="es">Ciencias Actuariales y Financieras</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Detection of interacting variables for generalized linear models via neural networks</dc:title>
<dc:relation xml:lang="es">En: European Actuarial Journal. - Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022. - 15/08/2024 Volumen 14 - Número 2 - agosto 2024 , p. 551-580</dc:relation>
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