Enhancing claim classification with feature extraction from anomaly-detection-derived routine and peculiarity profiles |
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Authors: | Francis Duval Jean-Philippe Boucher Mathieu Pigeon |
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Institution: | 1. Chaire Co-operators en analyse des risques actuariels, Département de mathématiques, Université du Québec à 2. Montréal, Montréal, Québec, Canada |
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Abstract: | Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection (AD) to vehicles' trip summaries, we develop a method allowing to derive a “routine” and a “peculiarity” anomaly profile for each vehicle. To this end, AD algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared with the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared with trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve the classification. |
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Keywords: | automobile insurance claim classification driving habits feature extraction supervised learning telematics car driving data unsupervised anomaly detection |
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