Artikel
Address identification using telematics: An algorithm to identify dwell locations
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 3 ; Pages: 1-12 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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telematics
address identification
POI
machine learning
mean shift clustering
DBSCAN clustering
fraud detection
- Event
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Geistige Schöpfung
- (who)
-
Grumiau, Christopher
Mostoufi, Mina
Pavlioglou, Solon
Verdonck, Tim
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
-
2020
- DOI
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doi:10.3390/risks8030092
- Handle
- Last update
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10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Grumiau, Christopher
- Mostoufi, Mina
- Pavlioglou, Solon
- Verdonck, Tim
- MDPI
Time of origin
- 2020
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