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Address Identification Using Telematics: An Algorithm to Identify Dwell Locations

Author

Listed:
  • Christopher Grumiau

    (Allianz Benelux, 1000 Brussels, Belgium)

  • Mina Mostoufi

    (Allianz Benelux, 1000 Brussels, Belgium)

  • Solon Pavlioglou

    (Allianz Benelux, 1000 Brussels, Belgium)

  • Tim Verdonck

    (Department of Mathematics (Faculty of Science), University of Antwerp, 2000 Antwerpen, Belgium
    Department of Mathematics (Faculty of Science), Katholieke Universiteit Leuven, 3000 Leuven, Belgium)

Abstract

In this work, a method is proposed for exploiting the predictive power of a geo-tagged dataset as a means of identification of user-relevant points of interest (POI). The proposed methodology is subsequently applied in an insurance context for the automatic identification of a driver’s residence address, solely based on his pattern of movements on the map. The analysis is performed on a real-life telematics dataset. We have anonymized the considered dataset for the purpose of this study to respect privacy regulations. The model performance is evaluated based on an independent batch of the dataset for which the address is known to be correct. The model is capable of predicting the residence postal code of the user with a high level of accuracy, with an f1 score of 0.83. A reliable result of the proposed method could generate benefits beyond the area of fraud, such as general data quality inspections, one-click quotations, and better-targeted marketing.

Suggested Citation

  • Christopher Grumiau & Mina Mostoufi & Solon Pavlioglou & Tim Verdonck, 2020. "Address Identification Using Telematics: An Algorithm to Identify Dwell Locations," Risks, MDPI, vol. 8(3), pages 1-12, September.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:3:p:92-:d:407190
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    References listed on IDEAS

    as
    1. Marjan Qazvini, 2019. "On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study," Risks, MDPI, vol. 7(3), pages 1-17, June.
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