Integration of traditional and telematics data for efficient insurance claims prediction

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      <subfield code="a">Integration of traditional and telematics data for efficient insurance claims prediction</subfield>
      <subfield code="c">Hashan Peiris [et al]</subfield>
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      <subfield code="a">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</subfield>
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      <subfield code="g">15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.263-279</subfield>
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      <subfield code="u">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</subfield>
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