Address Identification Using Telematics: An Algorithm to Identify Dwell Locations

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.

ENEX-FP: A BERT-Based Address Recognition Model

“…The approach put forward in [16] by Paolo Nesi et al addresses recognition by using techniques such as pattern matching, clustering and NLP to geolocate Web domains and businesses, with Precision and Recall both above 0.90; the system exhibits excellent skills for extracting pertinent information about the geographic location of the studied web domains. Grumiau Christopher et al [17] proposed using the predictive power of geotagged datasets to identify users' relevant points of interest (POIs). These works have some impact on address recognition.…”