Integration of traditional and telematics data for efficient insurance claims prediction

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<title>Integration of traditional and telematics data for efficient insurance claims prediction</title>
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<abstract displayLabel="Summary">While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features. To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset</abstract>
<note type="statement of responsibility">Hashan Peiris [et al]</note>
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<topic>Mercado de seguros</topic>
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<topic>Seguros</topic>
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<topic>Seguro de automóviles</topic>
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<topic>Telemática</topic>
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<url displayLabel="electronic resource" usage="primary display">https://www.cambridge.org/core/journals/astin-bulletin-journal-of-the-iaa/article/integration-of-traditional-and-telematics-data-for-efficient-insurance-claims-prediction/2D72787C1DE75B6DF0FECC90E6414B40</url>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
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<text>15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.263-279</text>
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