Transfer learning in the actuarial domain : foundations and applications

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<dc:creator>Kim, Youngsun </dc:creator>
<dc:creator>Bauer, Daniel</dc:creator>
<dc:date>2026-03-16</dc:date>
<dc:description xml:lang="es">Sumario: The article systematically analyzes the use of transfer learning in the actuarial field, particularly in the prediction of claim frequency when labeled data are scarce. It presents formal definitions, typologies, and methodological approaches, linking them to classical concepts such as credibility theory. Through empirical applications in automobile insurance, instance-based, feature-based, and parameter-based methods are evaluated. The results show improvements in accuracy and stability compared to traditional models. The study positions transfer learning as a relevant tool for modern actuarial analysis</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/190499.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Machine learning</dc:subject>
<dc:subject xml:lang="es">Modelos predictivos</dc:subject>
<dc:subject xml:lang="es">Seguro de automóviles</dc:subject>
<dc:subject xml:lang="es">Inteligencia artificial</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Transfer learning in the actuarial domain : foundations and applications</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 16/03/2026 Tomo 30 Número 1 - 2026 , 23 p.</dc:relation>
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