Transfer learning in the actuarial domain : foundations and applications

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<title>Transfer learning in the actuarial domain</title>
<subTitle>: foundations and applications</subTitle>
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<namePart>Bauer, Daniel</namePart>
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<abstract displayLabel="Summary">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</abstract>
<note type="statement of responsibility">Youngsun Kim and Daniel Bauer</note>
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<topic>Cálculo actuarial</topic>
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<topic>Machine learning</topic>
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<topic>Modelos predictivos</topic>
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<topic>Seguro de automóviles</topic>
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<topic>Inteligencia artificial</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
<identifier type="local">MAP20077000239</identifier>
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<text>16/03/2026 Tomo 30 Número 1 - 2026 , 23 p.</text>
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