Address Identification Using Telematics: An Algorithm to Identify Dwell Locations

Christopher Grumiau, Mina Mostoufi, Solon Pavlioglou and Tim Verdonck
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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

Risks, 2020, vol. 8, issue 3, 1-12

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.

Keywords: telematics; address identification; POI; machine learning; mean shift clustering; DBSCAN clustering; fraud detection (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
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